import pandas as pd
import gc
import numpy as np
import math
import scipy.stats as sts
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels as stats
from pandas.tseries.holiday import USFederalHolidayCalendar as calendar
import datetime
import re
import shap

from sklearn import preprocessing
import xgboost as xgb
import lightgbm as lgb
from xgboost import XGBClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.metrics import roc_auc_score
import catboost
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import roc_auc_score
import matplotlib.gridspec as gridspec
%matplotlib inline

# Standard plotly imports
import chart_studio.plotly as py
import plotly.graph_objs as go
import plotly.tools as tls
from plotly.offline import iplot, init_notebook_mode
import cufflinks
import cufflinks as cf
import plotly.figure_factory as ff

# Using plotly + cufflinks in offline mode
init_notebook_mode(connected=True)
cufflinks.go_offline(connected=True)


import warnings
warnings.filterwarnings("ignore")

import gc
gc.enable()

import logging
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)

import os

Functions

def make_corr(variables, data, figsize=(10, 15)):
    if isinstance(variables, pd.DataFrame):
        variables = variables['Column Name'].tolist()

    cols = variables

    corr_matrix = data[cols].corr()

    # Create a heatmap with the specified figsize
    plt.figure(figsize=figsize)
    sns.heatmap(corr_matrix, cmap='RdBu_r', annot=True, center=0.0)

    plt.title('Correlation Heatmap for Columns Starting with C')
    plt.show()
    
# We will focus on each column in detail
# Uniqe Values, DTYPE, NUNIQUE, NULL_RATE
def column_details(regex, df):
  
    global columns
    columns=[col for col in df.columns if re.search(regex, col)]

    from colorama import Fore, Back, Style

    print('Unique Values of the Features:\nfeature: DTYPE, NUNIQUE, NULL_RATE\n')
    for i in df[columns]:
        color = Fore.RED if df[i].dtype =='float64' else Fore.BLUE if df[i].dtype =='int64' else Fore.GREEN
        print(f'{i}: {color} {df[i].dtype}, {df[i].nunique()}, %{round(df[i].isna().sum()/len(df[i])*100,2)}\n{Style.RESET_ALL}{pd.Series(df[i].unique()).sort_values().values}\n')

def null_values(df, rate=0):
    """a function to show null values with percentage"""
    nv=pd.concat([df.isnull().sum(), 100 * df.isnull().sum()/df.shape[0]],axis=1).rename(columns={0:'Missing_Records', 1:'Percentage (%)'})
    return nv[nv['Percentage (%)']>rate].sort_values('Percentage (%)', ascending=False)

#Plot Functions

def plot_col(col, df, figsize=(20, 6)):
    """
    Function to create a pair of subplots containing two graphs based on a specified column.
    
    Left Graph (First Subplot):
    - Draws a bar graph representing the percentage of Fraud cases with respect to the specified column.
    - Uses two colors (0 and 1) to represent Fraud and Non-Fraud cases.
    - Adds a second line graph on the same column, representing the percentage of Fraud cases.

    Right Graph (Second Subplot):
    - Draws a bar graph representing the number of unique values in the dataset based on the specified column.
    
    The purpose of this function is to visualize the relationship between Fraud status and the unique values in a specific column.
    
    :param col: Name of the column to be visualized.
    :param df: Dataset.
    :param figsize: Size of the created figure.
    """
    # Create a figure with two subplots
    fig, ax = plt.subplots(1, 2, figsize=figsize, sharey=True)

    # Left Graph: Bar graph and line graph for Fraud percentages
    plt.subplot(121)
    tmp = pd.crosstab(df[col], df['isFraud'], normalize='index') * 100
    tmp = tmp.reset_index()
    tmp.rename(columns={0: 'NoFraud', 1: 'Fraud'}, inplace=True)

    ax[0] = sns.countplot(x=col, data=df, hue='isFraud', 
                          order=np.sort(df[col].dropna().unique()))
    ax[0].tick_params(axis='x', rotation=90)

    ax_twin = ax[0].twinx()
    ax_twin = sns.pointplot(x=col, y='Fraud', data=tmp, color='black', order=np.sort(df[col].dropna().unique()))

    ax[0].grid()

    # Right Graph: Bar graph for the number of unique values in the column
    plt.subplot(122)
    ax[1] = sns.countplot(x=df[col].dropna(),
                          order=np.sort(df[col].dropna().unique()))

    plt.show()


#correlation functions
# Remove the highly collinear features from data
def remove_collinear_features(x, threshold):
    '''
    Objective:
        Remove collinear features in a dataframe with a correlation coefficient
        greater than the threshold. Removing collinear features can help a model 
        to generalize and improves the interpretability of the model.

    Inputs: 
        x: features dataframe
        threshold: features with correlations greater than this value are removed

    Output: 
        dataframe that contains only the non-highly-collinear features
    '''

    # Calculate the correlation matrix
    corr_matrix = x.corr()
    iters = range(len(corr_matrix.columns) - 1)
    drop_cols = []

    # Iterate through the correlation matrix and compare correlations
    for i in iters:
        for j in range(i+1):
            item = corr_matrix.iloc[j:(j+1), (i+1):(i+2)]
            col = item.columns
            row = item.index
            val = abs(item.values)

            # If correlation exceeds the threshold
            if val >= threshold:
                # Print the correlated features and the correlation value
                # print(col.values[0], "|", row.values[0], "|", round(val[0][0], 2))
                drop_cols.append(col.values[0])

    # Drop one of each pair of correlated columns
    drops = set(drop_cols)
    x = x.drop(columns=drops)

    return drops


# References:
# https://towardsdatascience.com/the-search-for-categorical-correlation-a1cf7f1888c9
# https://en.wikipedia.org/wiki/Cram%C3%A9r%27s_V

def cramers_v(x, y):
    """ calculate Cramers V statistic for categorial-categorial association.
        uses correction from Bergsma and Wicher, 
        Journal of the Korean Statistical Society 42 (2013): 323-328
    """
    confusion_matrix = pd.crosstab(x,y)
    chi2 = sts.chi2_contingency(confusion_matrix)[0]
    n = confusion_matrix.sum().sum()
    phi2 = chi2/n
    r,k = confusion_matrix.shape
    phi2corr = max(0, phi2-((k-1)*(r-1))/(n-1))
    rcorr = r-((r-1)**2)/(n-1)
    kcorr = k-((k-1)**2)/(n-1)
    return np.sqrt(phi2corr/min((kcorr-1),(rcorr-1)))

#outlier functions
def simplify_column(col, df, threshold=0.005, value='mode'):
  df[col] = df[col].replace(df[col].value_counts(dropna=True)[df[col].value_counts(dropna=True, normalize=True)<threshold].index,df[col].mode()[0] if value=='mode' else 'other')
  return df[col]

def identify_collinear_categorical_features(df, columns, threshold):
    """
    Objective:
        Identify collinear categorical features in a dataframe with Cramér's V greater than the threshold.

    Inputs:
        df: dataframe
        columns: list of column names to check for collinearity
        threshold: features with Cramér's V greater than this value are considered collinear

    Output:
        list of columns to drop
    """
    # Create an empty DataFrame to store the results
    cramers_v_matrix = pd.DataFrame(index=columns, columns=columns, dtype=float)

    # Fill in the Cramér's V values for each pair of columns
    for col1 in columns:
        for col2 in columns:
            cramers_v_matrix.loc[col1, col2] = cramers_v(df[col1], df[col2])

    # Identify columns to drop based on Cramér's V threshold
    drop_cols = set()
    for i, col1 in enumerate(columns):
        for j, col2 in enumerate(columns):
            if i < j and cramers_v_matrix.loc[col1, col2] > threshold:
                drop_cols.add(col2)

    return list(drop_cols)

def remove_collinear_categorical_features(df, drop_cols):
    """
    Objective:
        Remove collinear categorical features from a dataframe.

    Inputs:
        df: dataframe
        drop_cols: list of columns to drop

    Output:
        dataframe that contains only the non-highly-collinear features
    """
    # Drop the identified columns
    df = df.drop(columns=drop_cols)

    return df

#Encoders
# Frequency Encoding

def frequency_encoding(train, test, columns, self_encoding=False):
    for col in columns:
        df = pd.concat([train[[col]], test[[col]]])
        fq_encode = df[col].value_counts(dropna=False, normalize=True).to_dict()
        if self_encoding:
            train[col] = train[col].map(fq_encode)
            test[col]  = test[col].map(fq_encode)            
        else:
            train[col+'_freq_encoded'] = train[col].map(fq_encode)
            test[col+'_freq_encoded']  = test[col].map(fq_encode)
    return train, test

#Modeling
def plot_feature_importances(model, num=10, figsize=(20,10)):
    feature_imp = pd.Series(model.feature_importances_,index=X.columns).sort_values(ascending=False)[:num]
    plt.figure(figsize=figsize)
    sns.barplot(x=feature_imp, y=feature_imp.index)
    plt.title("Feature Importance")
    plt.show()

def remove_collinear_features(x, threshold):
    '''
    Objective:
        Remove collinear features in a dataframe with a correlation coefficient
        greater than the threshold. Removing collinear features can help a model 
        to generalize and improves the interpretability of the model.

    Inputs: 
        x: features dataframe
        threshold: features with correlations greater than this value are removed

    Output: 
        dataframe that contains only the non-highly-collinear features
    '''

    # Calculate the correlation matrix
    corr_matrix = x.corr()
    iters = range(len(corr_matrix.columns) - 1)
    drop_cols = []

    # Iterate through the correlation matrix and compare correlations
    for i in iters:
        for j in range(i+1):
            item = corr_matrix.iloc[j:(j+1), (i+1):(i+2)]
            col = item.columns
            row = item.index
            val = abs(item.values)

            # If correlation exceeds the threshold
            if val >= threshold:
                # Check distinct values for each correlated pair
                distinct_values_col = len(x[col[0]].unique())
                distinct_values_row = len(x[row[0]].unique())

                # Keep the one with more distinct values
                if distinct_values_col > distinct_values_row:
                    drop_cols.append(row.values[0])
                else:
                    drop_cols.append(col.values[0])

    # Drop one of each pair of correlated columns
    drops = set(drop_cols)
    x = x.drop(columns=drops)

    return drops

• Data is separated into two datasets: customer identity information and transaction information.
• Not all transactions are associated with available identities.
• Unique key for both tables is TransactionID. It is duplicated in transaction table, it is unique in identity table.

Transaction Dataset

# Importing transaction data
# We are standardizing the column types in accordance with the data definition.

# Define column names for the dataset
cols_t = ['TransactionID', 'TransactionDT', 'TransactionAmt',
   'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6',
   'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain',
   'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
   'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8',
   'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4',
   'M5', 'M6', 'M7', 'M8', 'M9']

# Generate column names for the 'V' features (V1 to V339)
cols_v = ['V'+str(x) for x in range(1, 340)]

# Define data types for the 'V' features as float32
types_v = {c: 'float32' for c in cols_v}

# Specify the columns that need to be converted to the 'object' data type
columns_to_convert_to_object = ['ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6', 
                                'addr1', 'addr2', 'P_emaildomain', 'R_emaildomain', 'M1', 'M2', 'M3', 'M4', 
                                'M5', 'M6', 'M7', 'M8', 'M9']

# Read the data from the CSV file into a DataFrame (train)
transaction = pd.read_csv(r'C:\Fraud_Data\data\train_transaction.csv',
                    usecols=cols_t+['isFraud']+cols_v, 
                    dtype={**types_v, **{col: 'object' for col in columns_to_convert_to_object}}, index_col='TransactionID')

# The `TransactionDT` feature represents a timedelta from a specific reference datetime, rather than an actual timestamp. 
# It measures the time elapsed since the reference datetime in a timedelta format.

# Getting a real format of transaction date

# Predefined start date
START_DATE = datetime.datetime.strptime('2017-11-30', '%Y-%m-%d')

# Define the date range
dates_range = pd.date_range(start='2017-10-01', end='2019-01-01')
us_holidays = calendar().holidays(start=dates_range.min(), end=dates_range.max())

# Create 'DT' column using the 'TransactionDT' column
transaction['DT'] = transaction['TransactionDT'].apply(lambda x: (START_DATE + datetime.timedelta(seconds=x)))

# Convert 'DT' column to 'DatetimeIndex' object
transaction['DT'] = pd.to_datetime(transaction['DT'])

# Sorting the DataFrame based on the 'DT' column
transaction = transaction.sort_values(by='DT')

TRAIN-TEST SPLIT BASED ON TRANSACTION DATE IN TRANSACTION DATA

# Splitting train-test (TRAIN 75% TEST 25%) We will then merge these tables with identity table by using TransactionID.
train_index = transaction.index[:3 * len(transaction) // 4]
test_index = transaction.index[3 * len(transaction) // 4:]

# Splitting train_transaction and test_transaction based on indices
train_transaction = transaction.loc[train_index]
test_transaction = transaction.loc[test_index]

train_transaction.head()

	isFraud	TransactionDT	TransactionAmt	ProductCD	card1	card2	card3	card4	card5	card6	...	V331	V332	V333	V334	V335	V336	V337	V338	V339	DT
TransactionID
2987000	0	86400	68.5	W	13926	NaN	150.0	discover	142.0	credit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017-12-01 00:00:00
2987001	0	86401	29.0	W	2755	404.0	150.0	mastercard	102.0	credit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017-12-01 00:00:01
2987002	0	86469	59.0	W	4663	490.0	150.0	visa	166.0	debit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017-12-01 00:01:09
2987003	0	86499	50.0	W	18132	567.0	150.0	mastercard	117.0	debit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	2017-12-01 00:01:39
2987004	0	86506	50.0	H	4497	514.0	150.0	mastercard	102.0	credit	...	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2017-12-01 00:01:46

5 rows × 394 columns

Identity Dataset

# Importing identity data
# Define column names for the dataset
cols_t = ['TransactionID','DeviceInfo','DeviceType','id_38','id_37','id_36','id_35','id_34','id_33','id_32','id_31','id_30','id_29','id_28',
          'id_27','id_26','id_25','id_24','id_23','id_22','id_21','id_20','id_19','id_18','id_17','id_16','id_15','id_14','id_13',
          'id_12','id_11','id_10','id_09','id_08','id_07','id_06','id_05','id_04','id_03','id_02','id_01']

# Specify the columns that need to be converted to the 'object' data type
columns_to_convert_to_object = ['DeviceInfo','DeviceType','id_38','id_37','id_36','id_35','id_34','id_33','id_32','id_31','id_30','id_29','id_28',
          'id_27','id_26','id_25','id_24','id_23','id_22','id_21','id_20','id_19','id_18','id_17','id_16','id_15','id_14','id_13',
          'id_12']

# Read the data
identity = pd.read_csv(
    r'C:\Fraud_Data\data\train_identity.csv',
    usecols=cols_t,
    dtype=dict.fromkeys(columns_to_convert_to_object, 'object'), 
    index_col='TransactionID'
)

identity.head()

	id_01	id_02	id_03	id_04	id_05	id_06	id_07	id_08	id_09	id_10	...	id_31	id_32	id_33	id_34	id_35	id_36	id_37	id_38	DeviceType	DeviceInfo
TransactionID
2987004	0.0	70787.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	samsung browser 6.2	32.0	2220x1080	match_status:2	T	F	T	T	mobile	SAMSUNG SM-G892A Build/NRD90M
2987008	-5.0	98945.0	NaN	NaN	0.0	-5.0	NaN	NaN	NaN	NaN	...	mobile safari 11.0	32.0	1334x750	match_status:1	T	F	F	T	mobile	iOS Device
2987010	-5.0	191631.0	0.0	0.0	0.0	0.0	NaN	NaN	0.0	0.0	...	chrome 62.0	NaN	NaN	NaN	F	F	T	T	desktop	Windows
2987011	-5.0	221832.0	NaN	NaN	0.0	-6.0	NaN	NaN	NaN	NaN	...	chrome 62.0	NaN	NaN	NaN	F	F	T	T	desktop	NaN
2987016	0.0	7460.0	0.0	0.0	1.0	0.0	NaN	NaN	0.0	0.0	...	chrome 62.0	24.0	1280x800	match_status:2	T	F	T	T	desktop	MacOS

5 rows × 40 columns

# Check for duplicated Transaction IDs when TransactionID is the index - all transaction ids unique in identity table, this will be elobrated while merging the datasets
print('Length of Transaction IDs:', identity.index.shape[0])
print('Number of unique Transaction IDs:', identity.index.nunique())

Length of Transaction IDs: 144233
Number of unique Transaction IDs: 144233

Merging Transaction and Identity Data

# Merging datas

# Merge train_transaction and identity
train = pd.merge(train_transaction, identity, how='left', left_index=True, right_index=True)

# Merge test_transaction and identity
test = pd.merge(test_transaction, identity, how='left', left_index=True, right_index=True)

print("Train: ", train.shape)
print("Test: ", test.shape)

# Delete transaction, train_transaction, test_transaction, identity
del transaction, train_transaction, test_transaction, identity

Train:  (442905, 434)
Test:  (147635, 434)

# Train start-end date
print('min Transaction Date: ',min(train['DT'].values))
print('max Transaction Date: ',max(train['DT'].values))

min Transaction Date:  2017-12-01T00:00:00.000000000
max Transaction Date:  2018-04-09T04:03:25.000000000

# Test start-end date
print('min Transaction Date: ',min(test['DT'].values))
print('max Transaction Date: ',max(test['DT'].values))

min Transaction Date:  2018-04-09T04:04:25.000000000
max Transaction Date:  2018-05-31T23:58:51.000000000

# Check for the duplicated dates-train
print('length of Transaction Date',train['DT'].shape[0] )
print('length of unique Transaction Date', train['DT'].nunique())

length of Transaction Date 442905
length of unique Transaction Date 429087

# Check for the duplicated dates-test
print('length of Transaction Date',test['DT'].shape[0] )
print('length of unique Transaction Date', test['DT'].nunique())

length of Transaction Date 147635
length of unique Transaction Date 144262

train.head()

	isFraud	TransactionDT	TransactionAmt	ProductCD	card1	card2	card3	card4	card5	card6	...	id_31	id_32	id_33	id_34	id_35	id_36	id_37	id_38	DeviceType	DeviceInfo
TransactionID
2987000	0	86400	68.5	W	13926	NaN	150.0	discover	142.0	credit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2987001	0	86401	29.0	W	2755	404.0	150.0	mastercard	102.0	credit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2987002	0	86469	59.0	W	4663	490.0	150.0	visa	166.0	debit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2987003	0	86499	50.0	W	18132	567.0	150.0	mastercard	117.0	debit	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2987004	0	86506	50.0	H	4497	514.0	150.0	mastercard	102.0	credit	...	samsung browser 6.2	32.0	2220x1080	match_status:2	T	F	T	T	mobile	SAMSUNG SM-G892A Build/NRD90M

5 rows × 434 columns

# Performing garbage collection to release memory occupied by unused objects
gc.collect()

1496

#pickling datasets
#Save 'train' data to a pickle file named 'train_1.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_1.pkl')

#save 'test' data to a pickle file named 'test_1.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_1.pkl')

# Read the 'train_1.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_1.pkl')

# Read the 'test_1.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_1.pkl')

# Just to preserve the original train I did below. original train will be now train_original

# Copy the DataFrame and rename it
train_copy = train.copy()  # Create a copy of the original DataFrame and name it train_copy
train_original = train.copy()  # Create another copy of the original DataFrame and name it train_original

# Rename train by assigning the copy's content to it
train = train_copy.copy()  # Rename train by assigning the content of train_copy to it

Target Variable Distribution

The original dataset is characterized by a significant class imbalance, predominantly consisting of non-fraudulent transactions. This imbalance, where only 3.5% of transactions are labeled as fraud, poses a challenge for developing accurate predictive models and conducting analyses. Utilizing such an imbalanced dataset as the basis for machine learning models may lead to substantial errors and the risk of overfitting.

The risk of overfitting arises from the potential for algorithms to incorrectly assume that the majority of transactions are not fraudulent. In contrast to making assumptions, the primary objective is to develop models capable of identifying patterns indicative of fraudulent activities.

In the machine learning context, class imbalance denotes a considerable disparity in the number of data points representing different classes. Addressing this imbalance is paramount to ensure that models are not misled by the prevalence of non-fraudulent instances. The goal is to enable models to accurately identify patterns associated with fraudulent transactions.

In the training set, approximately 96.49% of transactions are labeled as non-fraudulent (isFraud==0), while around 3.51% are identified as fraudulent (isFraud==1). Similarly, in the test set, approximately 96.55% of transactions are non-fraudulent, and about 3.45% are labeled as fraudulent.

This class distribution indicates a high prevalence of non-fraudulent transactions in both the training and test sets, with a relatively small percentage of transactions being classified as fraudulent. This reinforces the presence of a significant class imbalance, which should be considered when developing and evaluating predictive models.

# Train Data
# Count the occurrences of each class in the 'isFraud' column
class_counts = train['isFraud'].value_counts()

# Calculate the percentage distribution
class_percentages = class_counts / len(train) * 100

# Plot the class distribution using Matplotlib
plt.figure(figsize=(8, 6))
sns.barplot(x=class_counts.index, y=class_counts.values, palette="mako")

# Adding percentages above the bars
for i, value in enumerate(class_counts.values):
    plt.text(i, value + 50, f'{class_percentages[i]:.2f}%', ha='center', va='bottom', fontsize=10, color='black')

plt.title("Train Data Imbalance - isFraud")
plt.xlabel("Class")
plt.ylabel("Count")
plt.show()

# Test Data
# Count the occurrences of each class in the 'isFraud' column
class_counts = test['isFraud'].value_counts()

# Calculate the percentage distribution
class_percentages = class_counts / len(test) * 100

# Plot the class distribution using Matplotlib
plt.figure(figsize=(8, 6))
sns.barplot(x=class_counts.index, y=class_counts.values, palette="mako")

# Adding percentages above the bars
for i, value in enumerate(class_counts.values):
    plt.text(i, value + 50, f'{class_percentages[i]:.2f}%', ha='center', va='bottom', fontsize=10, color='black')

plt.title("Test Data Imbalance - isFraud")
plt.xlabel("Class")
plt.ylabel("Count")
plt.show()

del class_counts
gc.collect()

5669

Handling Missing Values, Feature Elimination By 3 Criterias

Datasets have too much missing values.

# Total missing values of the train data
missing_count = train.isnull().sum()
cell_counts = np.product(train.shape)
missing_sum = missing_count.sum()
print ("%",(round(missing_sum/cell_counts,2)) * 100)

% 45.0

# Total missing values of the test data
missing_count = test.isnull().sum()
cell_counts = np.product(test.shape)
missing_sum = missing_count.sum()
print ("%",(round(missing_sum/cell_counts,2)) * 100)

% 45.0

# columns with no nulls of train (20 columns have no nulls, rest have)
null_counts = train.isnull().sum()

columns_with_no_null = null_counts[null_counts == 0].index

print("\nColumns with No Null Values-train:")
print(columns_with_no_null)

Columns with No Null Values-train:
Index(['isFraud', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1',
       'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
       'C12', 'C13', 'C14', 'DT'],
      dtype='object')

# columns with no nulls of test
null_counts = test.isnull().sum()

columns_with_no_null = null_counts[null_counts == 0].index

print("\nColumns with No Null Values-test:")
print(columns_with_no_null)

Columns with No Null Values-test:
Index(['isFraud', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1',
       'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11',
       'C12', 'C13', 'C14', 'V279', 'V280', 'V284', 'V285', 'V286', 'V287',
       'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V297', 'V298', 'V299',
       'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310',
       'V311', 'V312', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'DT'],
      dtype='object')

del missing_count, cell_counts, missing_sum, null_counts, columns_with_no_null

It has been observed that a significant number of columns exhibit a pattern of "correlated missing values," where certain rows share missing values in corresponding positions across multiple columns. Most of them are in the same variable group, this will be a clue for highly correlated variables in the same groups to eliminate later.

# Check for null percentage correlated columns-train
# Create a dictionary to store columns with null percentages > 10%
null_percentage_dict = {}
nan_counts = train.isnull().sum()

for col in train.columns:
    null_percent = (nan_counts[col] / len(train)) * 100
    if null_percent >= 10:
        null_percentage_dict[null_percent] = null_percentage_dict.get(null_percent, []) + [col]

# Sort the dictionary by null percentages
sorted_null_percentage_dict = {k: v for k, v in sorted(null_percentage_dict.items(), reverse=True)}

for null_percent, columns in sorted_null_percentage_dict.items():
    print(f"####### Null Percentage = {null_percent:.2f}%")
    print(columns)

####### Null Percentage = 99.16%
['id_24']
####### Null Percentage = 99.08%
['id_25']
####### Null Percentage = 99.08%
['id_07', 'id_08']
####### Null Percentage = 99.08%
['id_26']
####### Null Percentage = 99.08%
['id_21']
####### Null Percentage = 99.08%
['id_22', 'id_23', 'id_27']
####### Null Percentage = 93.60%
['D7']
####### Null Percentage = 93.11%
['dist2']
####### Null Percentage = 91.95%
['id_18']
####### Null Percentage = 89.48%
['D13']
####### Null Percentage = 89.29%
['D14']
####### Null Percentage = 88.68%
['D12']
####### Null Percentage = 88.37%
['id_03', 'id_04']
####### Null Percentage = 87.44%
['D6']
####### Null Percentage = 86.74%
['D8', 'D9', 'id_09', 'id_10']
####### Null Percentage = 86.57%
['id_33']
####### Null Percentage = 85.60%
['id_30']
####### Null Percentage = 85.60%
['id_32']
####### Null Percentage = 85.59%
['id_34']
####### Null Percentage = 85.14%
['id_14']
####### Null Percentage = 84.82%
['V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166']
####### Null Percentage = 84.75%
['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339']
####### Null Percentage = 78.49%
['DeviceInfo']
####### Null Percentage = 77.38%
['id_13']
####### Null Percentage = 76.65%
['id_16']
####### Null Percentage = 76.23%
['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278']
####### Null Percentage = 75.56%
['R_emaildomain']
####### Null Percentage = 75.29%
['id_05', 'id_06']
####### Null Percentage = 74.86%
['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216']
####### Null Percentage = 74.84%
['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210']
####### Null Percentage = 74.84%
['id_20']
####### Null Percentage = 74.83%
['id_19']
####### Null Percentage = 74.83%
['id_17']
####### Null Percentage = 74.66%
['id_31']
####### Null Percentage = 74.62%
['DeviceType']
####### Null Percentage = 74.61%
['id_02']
####### Null Percentage = 74.59%
['id_11', 'id_15', 'id_28', 'id_29', 'id_35', 'id_36', 'id_37', 'id_38']
####### Null Percentage = 74.50%
['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272']
####### Null Percentage = 74.02%
['id_01', 'id_12']
####### Null Percentage = 64.81%
['M7']
####### Null Percentage = 64.80%
['M8', 'M9']
####### Null Percentage = 61.25%
['dist1']
####### Null Percentage = 60.23%
['M5']
####### Null Percentage = 53.82%
['D5']
####### Null Percentage = 53.02%
['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']
####### Null Percentage = 51.88%
['M1', 'M2', 'M3']
####### Null Percentage = 49.40%
['D2']
####### Null Percentage = 48.26%
['M4']
####### Null Percentage = 46.54%
['D3']
####### Null Percentage = 30.45%
['M6']
####### Null Percentage = 29.41%
['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52']
####### Null Percentage = 29.41%
['D4']
####### Null Percentage = 16.33%
['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94']
####### Null Percentage = 16.33%
['D15']
####### Null Percentage = 15.55%
['P_emaildomain']
####### Null Percentage = 14.59%
['V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74']
####### Null Percentage = 14.36%
['V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34']
####### Null Percentage = 14.35%
['D10']
####### Null Percentage = 11.45%
['addr1', 'addr2']

# Check for null percentage correlated columns-test
# Create a dictionary to store columns with null percentages > 10%
null_percentage_dict = {}
nan_counts = test.isnull().sum()

for col in test.columns:
    null_percent = (nan_counts[col] / len(test)) * 100
    if null_percent >= 10:
        null_percentage_dict[null_percent] = null_percentage_dict.get(null_percent, []) + [col]

# Sort the dictionary by null percentages
sorted_null_percentage_dict = {k: v for k, v in sorted(null_percentage_dict.items(), reverse=True)}

for null_percent, columns in sorted_null_percentage_dict.items():
    print(f"####### Null Percentage = {null_percent:.2f}%")
    print(columns)

####### Null Percentage = 99.32%
['id_24']
####### Null Percentage = 99.27%
['id_25']
####### Null Percentage = 99.26%
['id_21']
####### Null Percentage = 99.26%
['id_07', 'id_08']
####### Null Percentage = 99.26%
['id_22', 'id_23', 'id_26', 'id_27']
####### Null Percentage = 95.18%
['dist2']
####### Null Percentage = 93.59%
['id_18']
####### Null Percentage = 92.84%
['D7']
####### Null Percentage = 90.65%
['id_30']
####### Null Percentage = 90.65%
['id_32', 'id_33']
####### Null Percentage = 90.53%
['id_34']
####### Null Percentage = 90.35%
['id_14']
####### Null Percentage = 90.11%
['D12']
####### Null Percentage = 90.04%
['V138', 'V139', 'V140', 'V141', 'V142', 'V146', 'V147', 'V148', 'V149', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V161', 'V162', 'V163']
####### Null Percentage = 90.04%
['V143', 'V144', 'V145', 'V150', 'V151', 'V152', 'V159', 'V160', 'V164', 'V165', 'V166']
####### Null Percentage = 90.02%
['D14']
####### Null Percentage = 89.98%
['V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339']
####### Null Percentage = 89.95%
['id_03', 'id_04']
####### Null Percentage = 89.60%
['D13']
####### Null Percentage = 89.04%
['D8', 'D9', 'id_09', 'id_10']
####### Null Percentage = 88.09%
['D6']
####### Null Percentage = 84.16%
['DeviceInfo']
####### Null Percentage = 82.95%
['V217', 'V218', 'V219', 'V223', 'V224', 'V225', 'V226', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V235', 'V236', 'V237', 'V240', 'V241', 'V242', 'V243', 'V244', 'V246', 'V247', 'V248', 'V249', 'V252', 'V253', 'V254', 'V257', 'V258', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278']
####### Null Percentage = 82.43%
['id_16']
####### Null Percentage = 81.61%
['id_13']
####### Null Percentage = 81.41%
['id_05', 'id_06']
####### Null Percentage = 81.16%
['id_20']
####### Null Percentage = 81.13%
['id_19']
####### Null Percentage = 81.12%
['id_17']
####### Null Percentage = 81.00%
['id_31']
####### Null Percentage = 80.85%
['V167', 'V168', 'V172', 'V173', 'V176', 'V177', 'V178', 'V179', 'V181', 'V182', 'V183', 'V186', 'V187', 'V190', 'V191', 'V192', 'V193', 'V196', 'V199', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216']
####### Null Percentage = 80.77%
['V169', 'V170', 'V171', 'V174', 'V175', 'V180', 'V184', 'V185', 'V188', 'V189', 'V194', 'V195', 'V197', 'V198', 'V200', 'V201', 'V208', 'V209', 'V210']
####### Null Percentage = 80.75%
['DeviceType']
####### Null Percentage = 80.74%
['id_11', 'id_28', 'id_29']
####### Null Percentage = 80.74%
['id_02', 'id_15', 'id_35', 'id_36', 'id_37', 'id_38']
####### Null Percentage = 80.73%
['V220', 'V221', 'V222', 'V227', 'V234', 'V238', 'V239', 'V245', 'V250', 'V251', 'V255', 'V256', 'V259', 'V270', 'V271', 'V272']
####### Null Percentage = 80.32%
['R_emaildomain']
####### Null Percentage = 80.23%
['id_01', 'id_12']
####### Null Percentage = 56.72%
['M5']
####### Null Percentage = 54.87%
['dist1']
####### Null Percentage = 48.42%
['D5']
####### Null Percentage = 45.86%
['M4']
####### Null Percentage = 42.00%
['D2']
####### Null Percentage = 40.12%
['M7']
####### Null Percentage = 40.12%
['M8', 'M9']
####### Null Percentage = 38.43%
['D3']
####### Null Percentage = 30.12%
['D11', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']
####### Null Percentage = 27.98%
['M1', 'M2', 'M3']
####### Null Percentage = 26.21%
['V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52']
####### Null Percentage = 26.20%
['D4']
####### Null Percentage = 23.38%
['M6']
####### Null Percentage = 17.33%
['P_emaildomain']
####### Null Percentage = 11.40%
['V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94']
####### Null Percentage = 11.38%
['D15']
####### Null Percentage = 10.17%
['addr1', 'addr2']

# Drop columns by using 3 criteria:
# 1. If a column only has only one distinct value
# 2. If a column has more than 90% null values
# 3. If one of the categories in a column dominates more than 90% of the column
# we are looking for these criterias in train, then dropping the columns from both train and test

# Initialize lists to store columns to be dropped based on different criteria
one_value_cols, many_null_cols, big_top_value_cols = [], [], []

# Iterate through only the train DataFrame
for df in [train]:
    # Identify columns with only one distinct value
    one_value_cols += [col for col in df.columns if df[col].nunique() == 1]
    
    # Identify columns with more than 90% null values
    many_null_cols += [col for col in df.columns if df[col].isnull().sum() / df.shape[0] > 0.9]
    
    # Identify columns where a single value dominates more than 90%
    big_top_value_cols += [col for col in df.columns if df[col].value_counts(dropna=False, normalize=True).values[0] > 0.9]

# Combine the lists of columns to be dropped, removing duplicates using set
cols_to_drop = list(set(one_value_cols + many_null_cols + big_top_value_cols))

# Check if 'isFraud' is in the list of columns to be dropped, and remove it if present
if 'isFraud' in cols_to_drop:
    cols_to_drop.remove('isFraud')

# Drop the identified columns from the train DataFrame
train = train.drop(cols_to_drop, axis=1)
test = test.drop(cols_to_drop, axis=1)

# Print the number of features that are going to be dropped for being considered useless
print(f'{len(cols_to_drop)} features are going to be dropped for being useless')

66 features are going to be dropped for being useless

Transaction Date

# TransactionDT Dist for Train Transaction Data - Whole Data
time_val_whole = train['DT'].values

# Create a figure
plt.figure(figsize=(18, 5))

# Create a distribution plot for TransactionDT column - Train Data
plt.subplot(1, 3, 1)  # 1 row, 3 columns, plot at position 1
sns.kdeplot(time_val_whole, color='r', fill=True, edgecolor='black')
plt.title('Distribution of DT - Train Data', fontsize=14)
plt.xlabel('TransactionDT')

# TransactionDT Dist for Train - isFraud=0
time_val_no_fraud = train['DT'][train['isFraud'] == 0]

# TransactionDT Dist for Train - isFraud=1
time_val_fraud = train['DT'][train['isFraud'] == 1]

# Create subplots for isFraud=0 and isFraud=1
for i, time_val in enumerate([time_val_no_fraud, time_val_fraud], start=2):
    plt.subplot(1, 3, i)
    sns.kdeplot(time_val, color='r', fill=True, edgecolor='black')
    plt.title(f'Distribution of DT - isFraud={i-2}', fontsize=14)
    plt.xlabel('DT')
    

# Adjust layout
plt.tight_layout()

# Show the plots
plt.show()

# TransactionDT Dist for Train Transaction Data - Whole Data
time_val_whole = test['DT'].values

# Create a figure
plt.figure(figsize=(18, 5))

# Create a distribution plot for TransactionDT column - Whole Data
plt.subplot(1, 3, 1)  # 1 row, 3 columns, plot at position 1
sns.kdeplot(time_val_whole, color='r', fill=True, edgecolor='black')
plt.title('Distribution of DT - Train Data', fontsize=14)
plt.xlabel('TransactionDT')

# TransactionDT Dist for Train Transaction Data - isFraud=0
time_val_no_fraud = test['DT'][test['isFraud'] == 0]

# TransactionDT Dist for Train Transaction Data - isFraud=1
time_val_fraud = test['DT'][test['isFraud'] == 1]

# Create subplots for isFraud=0 and isFraud=1
for i, time_val in enumerate([time_val_no_fraud, time_val_fraud], start=2):
    plt.subplot(1, 3, i)
    sns.kdeplot(time_val, color='r', fill=True, edgecolor='black')
    plt.title(f'Distribution of DT - isFraud={i-2}', fontsize=14)
    plt.xlabel('DT')
    

# Adjust layout
plt.tight_layout()

# Show the plots
plt.show()

Transaction Amount

column_details(regex='TransactionAmt', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

TransactionAmt:  float64, 17111, %0.0
[2.5100000e-01 2.7200000e-01 2.9200000e-01 ... 6.0852300e+03 6.4509700e+03
 3.1937391e+04]

plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='TransactionAmt', x='isFraud', data=train)
plt.axhline(6500, color='red')
plt.title('Train')

plt.subplot(122)
sns.stripplot(y='TransactionAmt', x='isFraud', data=test)
plt.axhline(6500, color='red')
plt.title('Test')

Text(0.5, 1.0, 'Test')

max_transaction_amount = train['TransactionAmt'][train['TransactionAmt'] < 30000].max()
print(f"The maximum transaction amount below 30000 is: {max_transaction_amount}")

The maximum transaction amount below 30000 is: 6450.97

There are outliers in Transaction_Amount with isFraud == 0. These amounts are above 30k. We find the maximum amount below 30k ( 6450.97 ). We will capped the Train transaction with 6450.97 below code.

# Identify transactions with isFraud == 0 and TransactionAmt above 6450.97
condition = train['TransactionAmt'] > 6450.97

# Cap the TransactionAmt at 5368 for the identified transactions
train.loc[condition, 'TransactionAmt'] = 6450.97

train['TransactionAmt'].max()

6450.97

print('Avg Transaction Amount by Frauds-Train', train[train.isFraud==1]['TransactionAmt'].mean())
print('Avg Transaction Amount by Non-Frauds-Train', train[train.isFraud==0]['TransactionAmt'].mean())
print('Avg Transaction Amount-Train',train['TransactionAmt'].mean())
print('Avg Transaction Amount-Test', test['TransactionAmt'].mean() )

Avg Transaction Amount by Frauds-Train 147.32386647818544
Avg Transaction Amount by Non-Frauds-Train 133.7389101726486
Avg Transaction Amount-Train 134.2162646278547
Avg Transaction Amount-Test 137.11464901954145

The averages of TransactionAmt of train and test datasets are nearly same. The average of the fraud transactions(147.32) is bigger than the average of the non-fraud transactions(133.73). The average transaction amount for fraudulent transactions appears to be higher than that for legitimate transactions. Statistically, the average transaction amount for fraudulent transactions is 147.32 units, whereas the average amount for legitimate transactions is 133.74 units. This observation indicates that fraudulent transactions tend to involve higher amounts

#Distribution of TransactionAmt-Train
time_val = train['TransactionAmt'].values

plt.figure(figsize=(18, 4))
sns.distplot(time_val, color='r')
plt.title('Distribution of TransactionAmt-Train', fontsize=14)
plt.xlim([min(time_val)-1000, max(time_val)])  

# Show the plot
plt.show()

#Distribution of TransactionAmt-Train
time_val = test['TransactionAmt'].values

plt.figure(figsize=(18, 4))
sns.distplot(time_val, color='r')
plt.title('Distribution of TransactionAmt-Train', fontsize=14)
plt.xlim([min(time_val)-1000, max(time_val)])  

# Show the plot
plt.show()

plt.figure(figsize=(15,8))
plt.suptitle('Time of Transaction vs Amount by isFraud')
fraud_mean, nonfraud_mean = train[train.isFraud=='1']['TransactionAmt'].mean(), train[train.isFraud=='0']['TransactionAmt'].mean()
sns.scatterplot(x=train['DT'], y=train['TransactionAmt'], data=train, hue='isFraud', size="isFraud", sizes=(200, 20))
plt.axhline(y=fraud_mean ,color='red',label=f'fraud mean:{round(fraud_mean,2)}')
plt.axhline(y=nonfraud_mean, color='green',label=f'non-froud mean:{round(nonfraud_mean,2)}')
plt.legend()

plt.yscale('log')
plt.show()

ProductCD : product code, the product for each transaction (nominal categorical)

for df in [train, test]:
  column_details(regex='ProductCD', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

ProductCD:  object, 5, %0.0
['C' 'H' 'R' 'S' 'W']

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

ProductCD:  object, 5, %0.0
['C' 'H' 'R' 'S' 'W']

# Distribution of ProductCD column-Train
plot_col('ProductCD', df=train)

train.groupby('ProductCD')['isFraud'].mean()

ProductCD
C    0.113070
H    0.045851
R    0.035957
S    0.061235
W    0.020594
Name: isFraud, dtype: float64

• C, H, R, S, W values are unknown from data definition.

Probably their meaning:

• C (Credit): Credit card transactions
• H (Debit): Debit card or ATM card transactions
• R (Charge Card): Charge card transactions
• S (Cash): Cash transactions
• W (Wallet): Transactions made with digital wallets or payment applications

'W' has the highest frequency, while 'S' has the lowest. Most fraud activities realized by C product code. (11%) Least fraud activities with W product ccode. (2%) This could indicate that a C product category is more strongly associated with fraud. The probabilities of fraud for other categories (H, R, S, W) are lower, but the contribution of these categories may still be significant.

# Target Encoding For ProductCD (taking into account the average of the target feature in train set)
temp_dict = train.groupby(['ProductCD'])['isFraud'].agg(['mean']).to_dict()['mean']

train['ProductCD_target_encoded'] = train['ProductCD'].replace(temp_dict)
test['ProductCD_target_encoded']  = test['ProductCD'].replace(temp_dict)

# original 'ProductCD' column dropped
train.drop('ProductCD', axis=1, inplace=True)
test.drop('ProductCD', axis=1, inplace=True)

gc.collect()

29506

Card1-Card6 : payment card information, such as card type, card category, issue bank, country (nominal categorical)

column_details(regex='^card\d', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

card1:  object, 12485, %0.0
['1000' '10000' '10003' ... '9997' '9998' '9999']

card2:  object, 500, %1.53
['100.0' '101.0' '102.0' '103.0' '104.0' '105.0' '106.0' '108.0' '109.0'
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card3:  object, 106, %0.19
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card4:  object, 4, %0.19
['american express' 'discover' 'mastercard' 'visa' nan]

card5:  object, 111, %0.69
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 '234.0' '236.0' '237.0' nan]

card6:  object, 4, %0.19
['charge card' 'credit' 'debit' 'debit or credit' nan]

cards = ['card1', 'card2', 'card3', 'card4', 'card5', 'card6']
for i in cards:
    print ("Unique ",i, " = ",train[i].nunique())

Unique  card1  =  12485
Unique  card2  =  500
Unique  card3  =  106
Unique  card4  =  4
Unique  card5  =  111
Unique  card6  =  4

Card4 and Card6 has 4 distinct value, we will observe the distributions of fraud activities in these columns.

# to show the most used Brand of electronic cards for fraud transactions in the train dataset
plot_col("card4", df=train)

train.groupby('card4')['isFraud'].mean()

card4
american express    0.027966
discover            0.064971
mastercard          0.035260
visa                0.034743
Name: isFraud, dtype: float64

# to show the most used method of transaction with cards for fraud activities in the train dataset
plot_col("card6", df=train)

Card1: The 'Card 1' column, originally designated as categorical, exhibits behavior akin to continuous data, featuring a substantial '13553' unique values.

# identify the differences in the 'card1' column between the 'train' and 'test' DataFrames
# old versions represent the unique values in the 'card1' column that exist in the 'train' but not in the 'test' DataFrame and new versions are vice versa.
old_versions_card1 = set(train['card1'].unique()) - set(test['card1'].unique())
new_versions_card1 = set(test['card1'].unique()) - set(train['card1'].unique())

print("Old versions of card1 (in train but not in test):", old_versions_card1)
print("New versions of card1 (in test but not in train):", new_versions_card1)

Old versions of card1 (in train but not in test): {'6947', '3537', '1844', '6468', '18175', '4671', '7140', '13639', '5359', '11440', '12744', '11327', '14580', '5848', '5568', '14920', '10169', '3403', '2992', '17882', '8716', '4649', '5480', '8261', '6461', '9955', '10628', '16467', '9119', '6842', '6964', '15247', '8026', '3692', '1625', '14663', '12331', '2855', '13878', '11766', '10557', '16556', '17505', '16492', '8529', '1732', '2513', '1001', '5378', '14508', '12441', '2970', '1339', '2433', '6787', '8574', '5328', '16715', '17190', '16938', '9791', '13440', '7024', '9851', '13812', '10899', '6010', '10606', '4890', '18024', '9388', '17163', '15229', '3817', '3860', '13304', '3358', '11742', '9927', '15548', '13212', '8126', '10805', '8045', '5390', '16765', '2086', '15481', '9467', '13709', '17033', '3656', '10739', '1883', '6436', '15660', '3609', '13723', '17801', '7869', '4397', '4762', '3082', '12027', '14333', '9814', '9401', '17448', '11694', '2563', '11823', '2575', 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New versions of card1 (in test but not in train): {'11591', '1981', '6006', '11583', '10901', '8344', '14812', '5603', '7783', '9272', '18056', '10320', '8152', '5742', '6966', '17982', '3965', '5019', '3006', '11931', '6858', '17595', '14358', '2570', '9601', '1247', '1026', '9089', '10600', '13361', '1467', '12706', '4804', '4459', '7678', '11842', '16864', '10781', '9877', '1385', '5020', '14210', '17745', '2358', '4669', '17938', '8205', '11271', '14726', '2104', '3021', '16448', '3776', '8134', '6266', '10597', '12814', '7608', '5030', '10173', '6440', '9935', '3823', '7761', '2514', '1900', '16478', '8281', '3616', '7589', '1380', '12611', '11435', '13942', '4313', '1952', '13845', '18079', '14701', '10560', '9115', '7818', '6839', '12375', '6950', '14450', '13447', '8975', '17664', '7734', '17339', '13872', '11928', '13835', '17597', '15645', '6391', '15150', '13074', '7647', '10326', '7597', '1511', '14029', '11405', '17248', '12502', '16134', '9647', '11695', '1014', '3296', '15615', '17180', '16643', '8667', '7236', '11171', '12961', '7752', '16402', '16284', '1309', '11532', '7985', '9256', '1105', '2017', '17234', '3548', '2221', '10830', '12903', '13358', '1509', '4677', '14023', '12537', '12909', '1831', '18115', '13861', '11514', '12591', '8756', '10231', '3968', '16801', '4026', '9156', '12146', '6255', '11340', '5386', '11018', '14997', '6446', '1867', '1518', '11338', '8017', '16814', '8029', '14671', '13092', '5851', '8631', '14802', '18135', '10507', '9107', '12246', '15279', '15918', '17533', '8237', '11658', '10316', '9614', '11394', '7342', '8597', '7450', '3495', '16171', '4041', '18247', '3662', '5021', '10911', '6891', '12563', '11367', '3440', '9654', '12546', '12662', '1672', '5716', '15100', '12685', '12165', '1211', '15407', '5498', '16296', '2663', '3309', '4404', '14291', '15069', '16260', '16340', '9670', '17858', '2370', '1045', '13439', '12919', '10436', '13059', '13889', '16673', '6367', '12936', '3151', '16667', '2669', '16922', '15263', '5531', '7746', '8659', '8571', '8544', '7742', '13506', '3169', '10617', '10845', '1994', '5644', '18321', '7954', '12773', '17274', '11452', '15120', '1108', '1149', '9123', '10498', '8621', '7439', '12516', '11283', '8330', '13966', '2214', '13404', '4187', '18325', '15838', '15804', '8178', '9024', '3202', '9846', '7914', '11016', '4340', '4689', '6268', '4154', '12324', '4246', '17081', '14352', '15304', '10286', '5659', '3207', '10813', '14678', '15252', '15450', '2677', '12349', '13194', '3220', '8324', '18296', '10227', '3833', '13937', '8419', '15101', '7766', '9020', '6120', '13632', '2163', '3909', '18287', '8309', '12550', '15774', '15881', '17671', '11497', '5579', '1046', '4308', '1768', '7430', '6962', '1887', '5707', '8094', '8702', '8465', '7060', '11081', '13730', '4528', '14865', '12471', '2305', '16749', '14939', '3306', '3503', '14248', '10575', '1291', '8259', '9585', '14775', '9078', '11562', '14807', '3018', '10482', '12875', '1298', '1050', '13436', '6665', '16223', '6314', '3825', '1872', '12292', '10362', '8062', '2140', '12066', '11490', '1182', '9420', '4896', '1383', '4727', '9374', '10823', '13113', '16082', '16924', '5444', '4365', '17160', '5858', '7558', '8645', '8228', '1099', '17298', '14309', '10963', '11588', '17246', '18330', '1854', '4840', '1766', '13584', '16497', '17815', '9757', '7964', '17068', '12047', '11854', '4550', '12022', '5379', '16741', '8568', '17349', '15690', '1571', '4318', '9859', '6919', '6797', '8654', '2319', '5896', '11772', '12853', '3740', '17695', '2462', '9461', '7072', '3956', '13333', '9636', '13925', '3462', '10671', '12626', '1466', '9598', '3015', '4037', '1215', '3443', '8878', '4986', '16280', '2533', '5932', '4454', '12708', '8996', '9733', '6264', '3255', '2737', '6518', '17062', '8151', '6825', '8144', '5684', '14489', '11166', '10077', '10383', '2004', '2323', '8166', '3183', '10298', '9570', '12622', '2546', '5675', '3363', '17614', '3558', '3922', '14404', '12224', '14703', '13349', '10293', '4897', '13753', '6088', '15529', '10824', '7603', '8634', '15206', '4537', '12432', '2352', '11057', '15414', '17925', '18323', '12596', '10738', '16029', '2775', '10918', '9147', '3951', '14994', '14702', '9148', '15607', '8093', '14957', '10281', '5917', '16442', '7702', '9349', '1948', '10163', '3128', '11361', '17640', '5649', '11076', '17083', '3053', '9481', '8025', '12966', '6009', '4197', '11741', '9671', '16851', '9219', '17997', '14360', '11631', '17428', '4170', '3615', '1926', '5799', '6342', '7059', '14773', '1943', '7384', '6509', '8276', '8297', '4172', '13302', '6198', '1230', '5429', '2149', '6723', '4953', '17146', '2645', '7331', '13871', '3357', '9205', '14888', '3323', '6484', '16942', '8220', '2856', '5464', '8227', '10619', '1733', '10233', '11381', '13800', '17340', '16332', '8326', '11762', '1757', '14587', '3862', '7747', '12504', '8450', '11819', '18038', '5756', '2316', '14476', '8410', '10865', '1840', '7002', '17716', '16598', '12671', '12279', '17837', '3493', '7992', '4582', '16226', '8265', '14056', '17277', '11300', '17912', '9530', '15047', '8950', '1432', '7396', '14445', '3739', '1041', '14999', '7910', '7228', '7763', '10962', '6368', '14434', '15175', '13485', '12406', '18025', '16651', '16625', '17697', '12767', '16083', '8110', '15789', '16078', '14146', '12675', '17323', '6113', '13403', '5176', '7802', '4783', '12333', '2636', '10472', '11437', '7310', '8943', '5279', '3671', '9826', '7341', '14200', '6971', '15758', '2954', '5704', '9217', '13542', '7425', '14844', '7353', '4297', '1947', '7967', '4019', '11439', '17616', '14183', '12971', '10678', '3374', '15749', '1961', '14008', '13166', '1194', '15872', '16700', '16304', '8570', '9788', '5335', '10779', '14359', '4420', '18181', '6121', '7928', '5324', '1703', '2268', '16212', '16288', '7418', '1797', '12864', '4760', '9000', '13399', '1540', '10760', '18284', '2983', '11803', '10364', '17080', '10897', '1491', '6420', '17972', '7933', '2522', '12742', '11549', '5960', '3872', '12456', '12122', '15827', '3859', '15649', '1538', '5528', '2389', '6711', '4895', '4777', '16392', '13286', '4439', '10991', '13635', '11543', '18308', '17994', '18336', '17315', '5534', '6826', '16933', '11353', '6643', '4673', '10793', '18278', '9475', '13779', '14050', '10841', '16719', '11958', '1891', '3942', '10202', '18290', '1640', '2421', '2967', '15972', '3079', '13107', '14458', '10965', '18205', '14707', '13902', '12181', '3415', '16376', '6769', '11252', '14739', '17961', '12752', '15193', '1866', '8710', '9883', '10132', '12112', '12164', '2834', '14416', '1204', '14613', '7849', '5190', '1580', '1053', '9184', '15526', '12315', '5139', '6876', '3894', '1685', '4634', '3061', '12162', '6257', '15456', '11882', '11005', '14091', '14809', '4131', '13197', '1512', '16690', '6664', '1848', '11668', '4626', '8490', '3677', '3497', '10887', '4891', '10730', '11441', '13019', '17845', '11328', '4551', '12661', '11428', '12464', '3339', '10136', '4422', '2161', '3491', '6018', '18313', '9577', '2711', '13654', '13080', '11241', '11091', '3770', '5797', '7536', '9896', '11891', '9532', '12938', '7607', '1032', '12819', '8660', '12328', '18160', '17462', '7189', '2190', '13658', '11220', '3805', '10844', '17989', '7767', '6131', '12202', '6148', '16336', '6650', '10146', '15384', '7261', '7741', '15251', '10422', '8447', '15054', '10556', '5839', '14161', '14381', '17775', '8982', '5697', '9454', '7824', '9405', '12984', '8715', '9910', '6311', '9283', '9792', '4678', '6099', '1126', '5250', '7186', '12197', '2145', '17426', '14350', '5654', '14448', '10826', '6362', '4517', '6682', '13255', '11798', '5847', '11264', '13105', '4903', '7026', '4295', '10525', '4016', '2505', '11767', '2668', '16109', '13938', '10821', '7010', '4412', '8530', '11446', '11495', '4023', '3092', '3073', '6494', '1449', '14593', '15126', '13761', '15117', '16860', '4870', '2788', '1541', '4781', '8299', '13949', '2164', '13394', '4163', '10085', '7317', '12760', '13781', '6469', '7199', '14148', '8896', '10653', '13741', '3420', '16123', '8767', '2174', '5573', '17802', '1351', '12885', '12837', '8985', '6706', '7388', '3513', '13894', '12840', '18352', '9933', '10259', '6298', '10101', '14606', '10656', '2736', '14399', '15490', '9690', '8594', '5776', '13186', '10999', '10290', '14020', '6840', '5269', '8957', '8125', '12183', '17351', '9738', '10324', '11240', '17468', '14777', '8889', '18192', '4363', '17015', '5752', '16553', '11739', '4150', '15974', '1849', '7522', '12585', '13216', '9698', '17627', '6972', '10718', '8366', '2372', '5115', '17629', '6838', '6340', '8145', '13265', '10328', '2780', '9612', '12228', '7227', '15550', '10593', '10477', '8286', '4220', '3098', '17459', '10091', '11610', '13594', '3049', '5227', '8108', '2576', '16615', '17242', '13063', '7421', '10959', '17655', '15049', '8728', '13146', '7087', '18241', '12652', '8963', '5856', '12871', '9211', '16185', '1048', '5375', '12503', '6291', '11765', '14867', '8342', '2859', '15549', '11621', '1040', '15973', '14378', '6320', '10660', '13084', '17476', '3619', '14648'}

len(old_versions_card1), len(new_versions_card1)

(5810, 1068)

# We synchronized test['card1] and train[card1]
# Replace values in 'card1' column of test DataFrame with NaN if they are in new_versions_card1
test['card1'] = test['card1'].apply(lambda x: np.nan if x in new_versions_card1 else x)

# Replace values in 'card1' column of train DataFrame with NaN if they are in old_versions_card1
train['card1'] = train['card1'].apply(lambda x: np.nan if x in old_versions_card1 else x)

# Countplot of the frequency of the unique value frequencies in card1-train
plt.figure(figsize=(30,6))
train.card1.value_counts().to_frame().value_counts().head(100).plot.bar()

<Axes: xlabel='count'>

# Countplot of the frequency of the unique value frequencies in card1-test
plt.figure(figsize=(30,6))
test.card1.value_counts().to_frame().value_counts().head(100).plot.bar()

<Axes: xlabel='count'>

# identify and gather rare values in the 'card1' column in both the 'train' and 'test' DataFrames based on a frequency criterion. 
# After identifying these rare values, we collect them into a set (lower frequency means if a category has repeated lower than 3 in the column)
rareCards=[]
for k, df in enumerate([train, test]):
  rare_cards = df.card1.value_counts()
  rare_cards = rare_cards.where(rare_cards<3).dropna().sort_index().index # ==> rare_cards<3 refers to lower frequency
  rareCards += list(rare_cards)

  print(f"{('TEST' if k else 'TRAIN')}")
  print(f"Number of unique in card1: {df.card1.nunique()}")
  print(f"Number of unique values with frequency less than 3 in card1: {len(rare_cards)}\n")
rareCards = set(rareCards)

TRAIN
Number of unique in card1: 6675
Number of unique values with frequency less than 3 in card1: 1163

TEST
Number of unique in card1: 6675
Number of unique values with frequency less than 3 in card1: 2760

# We replaced "rare card1 values" with Nan. if a card repeated lower than 3
for df in [train, test]:
  df['card1'] = df['card1'].apply(lambda x: np.nan if x in rareCards else x)

Card2...Card6

# Same replacement for remaining card columns (card2-card6)
for col in ['card2','card3','card4','card5','card6']: 
  old_versions_col= set(train[col].unique()) - set(test[col].unique())
  new_versions_col = set(test[col].unique()) - set(train[col].unique()) 
  test[col] =test[col].apply(lambda x: np.nan if x in new_versions_col else x)
  train[col] =train[col].apply(lambda x: np.nan if x in old_versions_col else x)

rareCards = []

# Specify the range of columns you want to process
columns_to_process = [f'card{i}' for i in range(2, 7)]  # Assumes 'card2' to 'card6'

for col in columns_to_process:
    for k, df in enumerate([train, test]):
        rare_cards = df[col].value_counts()
        rare_cards = rare_cards.where(rare_cards < 3).dropna().sort_index().index
        rareCards += list(rare_cards)

        print(f"{('TEST' if k else 'TRAIN')}")
        print(f"Number of unique in {col}: {df[col].nunique()}")
        print(f"Number of unique values with frequency less than 3 in {col}: {len(rare_cards)}\n")

rareCards = set(rareCards)

TRAIN
Number of unique in card2: 495
Number of unique values with frequency less than 3 in card2: 0

TEST
Number of unique in card2: 495
Number of unique values with frequency less than 3 in card2: 1

TRAIN
Number of unique in card3: 70
Number of unique values with frequency less than 3 in card3: 5

TEST
Number of unique in card3: 70
Number of unique values with frequency less than 3 in card3: 17

TRAIN
Number of unique in card4: 4
Number of unique values with frequency less than 3 in card4: 0

TEST
Number of unique in card4: 4
Number of unique values with frequency less than 3 in card4: 0

TRAIN
Number of unique in card5: 66
Number of unique values with frequency less than 3 in card5: 6

TEST
Number of unique in card5: 66
Number of unique values with frequency less than 3 in card5: 8

TRAIN
Number of unique in card6: 2
Number of unique values with frequency less than 3 in card6: 0

TEST
Number of unique in card6: 2
Number of unique values with frequency less than 3 in card6: 0

# low frequency cats will change into nans
columns_to_process = [f'card{i}' for i in range(2, 7)]  # Assumes 'card2' to 'card6'

for col in columns_to_process:
    for df in [train, test]:
        df[col] = df[col].apply(lambda x: np.nan if x in rareCards else x)

One hot encoding is a common technique used to handle nominal categorical data by converting each category into a binary column. While label encoding can be suitable for ordinal categorical data, it's not the best choice for nominal categorical data due to its inherent ordering. Frequency encoding and target encoding provides an effective solution for nominal categorical data. Because we have lots of distinct values in card1,2,3,5 columns(if we use target encoding most of the values will be too small near to 0 or will be 0) it is better to use frequency encoding by replacing each category with its observed frequency. We will use target encoder for card4(4 distinct values) and card6 (2 distinct values)

# List of columns to process (Number of unique values below 200) (taking into account the average of the target feature in train set)
columns_to_process = ['card3', 'card4', 'card5', 'card6']

# Loop through each column
for col in columns_to_process:
    # Calculate target encoding for the current column
    temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
    
    # Create a new column with the target encoding values
    train[f'{col}_target_encoded'] = train[col].replace(temp_dict)
    test[f'{col}_target_encoded'] = test[col].replace(temp_dict)

# Frequency encoding for card1 (Number of unique values above 200)
self_encode_False=['card1', 'card2']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)

# List of original columns to drop
columns_to_drop = ['card1', 'card2', 'card3', 'card4', 'card5', 'card6']

# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)

addr1 and addr2: Address information related to the transaction (nominal categorical)

Probably addr1 - subzone / add2 - Country

for df in [train, test]:
  column_details(regex='addr', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

addr1:  object, 321, %11.45
['100.0' '101.0' '102.0' '104.0' '105.0' '106.0' '110.0' '111.0' '112.0'
 '113.0' '117.0' '119.0' '120.0' '122.0' '123.0' '124.0' '125.0' '126.0'
 '127.0' '128.0' '129.0' '130.0' '131.0' '132.0' '133.0' '134.0' '137.0'
 '139.0' '141.0' '142.0' '143.0' '144.0' '145.0' '146.0' '148.0' '151.0'
 '152.0' '153.0' '154.0' '155.0' '156.0' '157.0' '158.0' '159.0' '160.0'
 '161.0' '162.0' '163.0' '164.0' '166.0' '167.0' '168.0' '170.0' '171.0'
 '172.0' '174.0' '177.0' '178.0' '180.0' '181.0' '182.0' '183.0' '184.0'
 '185.0' '187.0' '189.0' '190.0' '191.0' '193.0' '194.0' '195.0' '196.0'
 '198.0' '199.0' '200.0' '201.0' '202.0' '203.0' '204.0' '205.0' '208.0'
 '210.0' '211.0' '213.0' '214.0' '215.0' '216.0' '217.0' '218.0' '219.0'
 '220.0' '221.0' '223.0' '224.0' '225.0' '226.0' '227.0' '231.0' '232.0'
 '233.0' '234.0' '235.0' '236.0' '239.0' '241.0' '242.0' '243.0' '244.0'
 '247.0' '248.0' '249.0' '250.0' '251.0' '252.0' '253.0' '254.0' '255.0'
 '257.0' '258.0' '259.0' '260.0' '261.0' '262.0' '264.0' '265.0' '269.0'
 '270.0' '272.0' '274.0' '275.0' '276.0' '277.0' '278.0' '279.0' '280.0'
 '282.0' '283.0' '284.0' '290.0' '292.0' '294.0' '295.0' '296.0' '297.0'
 '298.0' '299.0' '300.0' '301.0' '302.0' '303.0' '304.0' '305.0' '306.0'
 '307.0' '308.0' '309.0' '310.0' '312.0' '313.0' '314.0' '315.0' '316.0'
 '321.0' '322.0' '323.0' '324.0' '325.0' '326.0' '327.0' '328.0' '329.0'
 '330.0' '331.0' '332.0' '333.0' '335.0' '337.0' '338.0' '339.0' '340.0'
 '341.0' '343.0' '345.0' '346.0' '347.0' '348.0' '349.0' '351.0' '352.0'
 '353.0' '356.0' '358.0' '359.0' '360.0' '361.0' '365.0' '366.0' '368.0'
 '369.0' '371.0' '372.0' '373.0' '374.0' '375.0' '376.0' '377.0' '379.0'
 '381.0' '382.0' '384.0' '385.0' '386.0' '387.0' '389.0' '390.0' '391.0'
 '393.0' '395.0' '396.0' '397.0' '399.0' '400.0' '401.0' '402.0' '403.0'
 '404.0' '406.0' '408.0' '409.0' '410.0' '411.0' '416.0' '417.0' '418.0'
 '420.0' '425.0' '426.0' '427.0' '428.0' '429.0' '430.0' '431.0' '432.0'
 '433.0' '434.0' '435.0' '436.0' '439.0' '441.0' '443.0' '444.0' '445.0'
 '446.0' '448.0' '450.0' '451.0' '452.0' '453.0' '454.0' '456.0' '458.0'
 '459.0' '462.0' '463.0' '464.0' '465.0' '466.0' '467.0' '468.0' '469.0'
 '470.0' '471.0' '472.0' '474.0' '476.0' '477.0' '478.0' '479.0' '482.0'
 '483.0' '485.0' '486.0' '488.0' '489.0' '491.0' '492.0' '493.0' '494.0'
 '496.0' '498.0' '499.0' '500.0' '501.0' '502.0' '503.0' '504.0' '505.0'
 '506.0' '508.0' '509.0' '511.0' '512.0' '513.0' '514.0' '515.0' '516.0'
 '518.0' '519.0' '520.0' '521.0' '522.0' '523.0' '526.0' '527.0' '528.0'
 '529.0' '530.0' '531.0' '535.0' '536.0' '540.0' nan]

addr2:  object, 69, %11.45
['10.0' '101.0' '102.0' '13.0' '14.0' '15.0' '16.0' '17.0' '18.0' '19.0'
 '20.0' '21.0' '22.0' '23.0' '24.0' '25.0' '26.0' '27.0' '28.0' '29.0'
 '30.0' '31.0' '32.0' '34.0' '35.0' '36.0' '38.0' '39.0' '40.0' '43.0'
 '44.0' '46.0' '47.0' '48.0' '49.0' '50.0' '52.0' '54.0' '57.0' '59.0'
 '60.0' '61.0' '62.0' '63.0' '65.0' '66.0' '68.0' '69.0' '70.0' '71.0'
 '72.0' '73.0' '74.0' '75.0' '76.0' '77.0' '78.0' '79.0' '82.0' '83.0'
 '84.0' '86.0' '87.0' '88.0' '89.0' '92.0' '96.0' '97.0' '98.0' nan]

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

addr1:  object, 115, %10.17
['104.0' '105.0' '106.0' '110.0' '122.0' '123.0' '125.0' '126.0' '130.0'
 '134.0' '143.0' '157.0' '158.0' '159.0' '160.0' '167.0' '170.0' '177.0'
 '181.0' '184.0' '191.0' '194.0' '199.0' '201.0' '202.0' '203.0' '204.0'
 '205.0' '206.0' '215.0' '216.0' '220.0' '225.0' '226.0' '231.0' '237.0'
 '238.0' '239.0' '242.0' '245.0' '247.0' '251.0' '253.0' '254.0' '259.0'
 '260.0' '264.0' '268.0' '269.0' '272.0' '275.0' '276.0' '277.0' '284.0'
 '286.0' '296.0' '299.0' '301.0' '303.0' '308.0' '310.0' '315.0' '318.0'
 '324.0' '325.0' '327.0' '330.0' '337.0' '343.0' '346.0' '348.0' '356.0'
 '384.0' '387.0' '404.0' '409.0' '410.0' '411.0' '418.0' '420.0' '426.0'
 '428.0' '432.0' '433.0' '435.0' '436.0' '441.0' '444.0' '445.0' '448.0'
 '452.0' '453.0' '457.0' '465.0' '469.0' '472.0' '476.0' '478.0' '479.0'
 '481.0' '483.0' '485.0' '491.0' '492.0' '494.0' '498.0' '499.0' '502.0'
 '507.0' '508.0' '511.0' '512.0' '515.0' '517.0' '536.0' nan]

addr2:  object, 27, %10.17
['100.0' '16.0' '18.0' '19.0' '26.0' '27.0' '31.0' '32.0' '34.0' '40.0'
 '43.0' '51.0' '54.0' '55.0' '57.0' '59.0' '60.0' '61.0' '62.0' '65.0'
 '68.0' '78.0' '87.0' '93.0' '94.0' '96.0' '98.0' nan]

train.groupby('addr1')['isFraud'].mean().sort_values(ascending=False).head(20)

addr1
305.0    0.666667
466.0    0.500000
471.0    0.500000
483.0    0.500000
501.0    0.500000
391.0    0.457143
260.0    0.400000
431.0    0.342105
432.0    0.281250
296.0    0.233553
399.0    0.200000
239.0    0.200000
161.0    0.194774
216.0    0.180000
426.0    0.172414
453.0    0.166667
171.0    0.166667
199.0    0.125000
479.0    0.125000
356.0    0.123457
Name: isFraud, dtype: float64

# First ten most frequent adddress1 values in train
print ("Unique Subzones = ",train['addr1'].nunique())

print('\nFirst Ten Address-2')
print('--------------------')
train.addr1.value_counts().head(9)

Unique Subzones =  321

First Ten Address-2
--------------------

addr1
299.0    35042
325.0    31781
204.0    31261
264.0    29697
330.0    18576
315.0    17250
441.0    15527
272.0    15108
123.0    11935
Name: count, dtype: int64

train.groupby('addr2')['isFraud'].mean().sort_values(ascending=False).head(20)

addr2
10.0    1.000000
82.0    1.000000
46.0    1.000000
92.0    1.000000
75.0    1.000000
38.0    0.666667
65.0    0.581081
36.0    0.500000
73.0    0.200000
68.0    0.111111
96.0    0.107011
60.0    0.096003
29.0    0.090909
32.0    0.072289
87.0    0.024207
84.0    0.000000
88.0    0.000000
59.0    0.000000
61.0    0.000000
62.0    0.000000
Name: isFraud, dtype: float64

print ("Unique Countries = ",train['addr2'].nunique())

print('\nFirst Ten Address-2')
print('--------------------')
train.addr2.value_counts().head(9)

Unique Countries =  69

First Ten Address-2
--------------------

addr2
87.0    388434
60.0      2677
96.0       542
32.0        83
65.0        74
16.0        48
31.0        45
19.0        29
26.0        20
Name: count, dtype: int64

# By combining addr1 and addr2 , we create a new addr column
for df in [train, test]:
    # Combine 'addr2' and 'addr1' columns as strings, separated by an underscore
    df['addr'] = (df['addr2'].astype(str) + '_' + df['addr1'].astype(str)).replace({'nan_nan': np.nan})

train.groupby('addr')['isFraud'].mean().sort_values(ascending=False).head(20)

addr
10.0_296.0    1.000000
46.0_296.0    1.000000
60.0_296.0    1.000000
92.0_296.0    1.000000
82.0_296.0    1.000000
75.0_296.0    1.000000
65.0_296.0    0.704918
38.0_296.0    0.666667
60.0_305.0    0.666667
36.0_296.0    0.500000
60.0_466.0    0.500000
60.0_471.0    0.500000
60.0_501.0    0.500000
60.0_483.0    0.500000
60.0_391.0    0.457143
87.0_260.0    0.400000
60.0_431.0    0.342105
96.0_432.0    0.281250
73.0_296.0    0.250000
60.0_239.0    0.200000
Name: isFraud, dtype: float64

print ("Unique Adresses = ",train['addr'].nunique())

print('\nFirst Ten Addresses')
print('--------------------')
train.addr.value_counts().head(9)

Unique Adresses =  409

First Ten Addresses
--------------------

addr
87.0_299.0    35033
87.0_325.0    31780
87.0_204.0    31260
87.0_264.0    29697
87.0_330.0    18573
87.0_315.0    17249
87.0_441.0    15525
87.0_272.0    15107
87.0_123.0    11933
Name: count, dtype: int64

# unique values for address columns in train
train['addr1'].nunique(), train['addr2'].nunique(), train['addr'].nunique()

(321, 69, 409)

# unique values for address columns in train
test['addr1'].nunique(), test['addr2'].nunique(), test['addr'].nunique()

(116, 28, 147)

# Frequency encoding for addr columns
self_encode_False=['addr']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)

self_encode_False=['addr1']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)

self_encode_False=['addr2']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)

# List of original columns to drop
columns_to_drop = ['addr', 'addr1', 'addr2']

# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)

Top 'addr1' Values by Fraud Rate: The 'addr1' values with the highest fraud rates are 305.0, 466.0, 471.0, 483.0, and 501.0. For example, transactions with 'addr1' equal to 305.0 have a fraud rate of 66.67%.

Top 'addr2' Values by Fraud Rate: The 'addr2' values with the highest fraud rates are 10.0, 82.0, 46.0, 92.0, and 75.0. For example, transactions with 'addr2' equal to 10.0 have a fraud rate of 100%.

Top Combined 'addr' Values by Fraud Rate: The combined 'addr' values (created by concatenating 'addr2' and 'addr1') with the highest fraud rates are 10.0_296.0, 46.0_296.0, 60.0_296.0, 92.0_296.0, and 82.0_296.0. For example, transactions with 'addr' equal to 10.0_296.0 have a fraud rate of 100%.

Insights: Certain combinations of 'addr1' and 'addr2' or the combined 'addr' exhibit higher fraud rates. For instance, the combination 10.0_296.0 appears to have a consistent fraud rate of 100% across 'addr1' and 'addr2'. These patterns may indicate potential areas of interest for further investigation or feature engineering. High fraud rates in specific 'addr' combinations could be indicative of fraudulent behavior or anomalies in those locations.

dist1 : The distance (numeric)

column_details(regex='^dist', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

dist1:  float64, 1744, %54.87
[0.000e+00 1.000e+00 2.000e+00 ... 7.136e+03 8.081e+03       nan]

plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='dist1', x='isFraud', data=train)
plt.axhline(5000, color='red')
plt.title('Train')

plt.subplot(122)
sns.stripplot(y='dist1', x='isFraud', data=test)
plt.axhline(5000, color='red')
plt.title('Test')

Text(0.5, 1.0, 'Test')

max_dist1 = train['dist1'][train['dist1'] < 5500].max()
print(f"The maximum dist1 below 5500 is: {max_dist1}")

The maximum dist1 below 5500 is: 5431.0

There are outliers in dist1 with isFraud == 0. These are above 5500. We find the maximum amount below 5500 ( 5431 ). We will capped the Train with 5432 below code.

# Identify transactions with isFraud == 0 and dist1 5431
condition = train['dist1'] > 5431

# Cap the dist1 at 5432 for the identified transactions
train.loc[condition, 'dist1'] = 5431

train['dist1'].max()

5431.0

P_emaildomain&R_emaildomain

P_emaildomain : categoric, 56 uniques It's possible to make subgroup feature from it or general group

R_emaildomain : categoric, 59 uniques It's possible to make subgroup feature from it or general group

# R_emaildomain
column_details(regex='R_emaildomain', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

R_emaildomain:  object, 60, %75.56
['aim.com' 'anonymous.com' 'aol.com' 'att.net' 'bellsouth.net'
 'cableone.net' 'centurylink.net' 'cfl.rr.com' 'charter.net' 'comcast.net'
 'cox.net' 'earthlink.net' 'embarqmail.com' 'frontier.com'
 'frontiernet.net' 'gmail' 'gmail.com' 'gmx.de' 'hotmail.co.uk'
 'hotmail.com' 'hotmail.de' 'hotmail.es' 'hotmail.fr' 'icloud.com'
 'juno.com' 'live.com' 'live.com.mx' 'live.fr' 'mac.com' 'mail.com'
 'me.com' 'msn.com' 'netzero.com' 'netzero.net' 'optonline.net'
 'outlook.com' 'outlook.es' 'prodigy.net.mx' 'protonmail.com' 'ptd.net'
 'q.com' 'roadrunner.com' 'rocketmail.com' 'sbcglobal.net' 'sc.rr.com'
 'scranton.edu' 'servicios-ta.com' 'suddenlink.net' 'twc.com'
 'verizon.net' 'web.de' 'windstream.net' 'yahoo.co.jp' 'yahoo.co.uk'
 'yahoo.com' 'yahoo.com.mx' 'yahoo.de' 'yahoo.es' 'yahoo.fr' 'ymail.com'
 nan]

#We produced 2 new columns with mail server and domain for R_emaildomain.
for df in [train, test]:
  df['R_emaildomain_1'] = df['R_emaildomain'].fillna('').apply(lambda x: x.split(".")[0]).replace({'':np.nan})
  df['R_emaildomain_2'] = df['R_emaildomain'].str.split('.', expand=True).iloc[:,1:].fillna('').apply(lambda x:('.'.join(x)).strip('.'), axis=1).replace({'':np.nan})

# P_emaildomain
column_details(regex='P_emaildomain', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

P_emaildomain:  object, 59, %15.55
['aim.com' 'anonymous.com' 'aol.com' 'att.net' 'bellsouth.net'
 'cableone.net' 'centurylink.net' 'cfl.rr.com' 'charter.net' 'comcast.net'
 'cox.net' 'earthlink.net' 'embarqmail.com' 'frontier.com'
 'frontiernet.net' 'gmail' 'gmail.com' 'gmx.de' 'hotmail.co.uk'
 'hotmail.com' 'hotmail.de' 'hotmail.es' 'hotmail.fr' 'icloud.com'
 'juno.com' 'live.com' 'live.com.mx' 'live.fr' 'mac.com' 'mail.com'
 'me.com' 'msn.com' 'netzero.com' 'netzero.net' 'optonline.net'
 'outlook.com' 'outlook.es' 'prodigy.net.mx' 'protonmail.com' 'ptd.net'
 'q.com' 'roadrunner.com' 'rocketmail.com' 'sbcglobal.net' 'sc.rr.com'
 'servicios-ta.com' 'suddenlink.net' 'twc.com' 'verizon.net' 'web.de'
 'windstream.net' 'yahoo.co.jp' 'yahoo.co.uk' 'yahoo.com' 'yahoo.com.mx'
 'yahoo.de' 'yahoo.es' 'yahoo.fr' 'ymail.com' nan]

# We produced 2 new columns with mail server and domain P_emaildomain.
for df in [train, test]:
  df['P_emaildomain_1'] = df['P_emaildomain'].fillna('').apply(lambda x: x.split(".")[0]).replace({'':np.nan})
  df['P_emaildomain_2'] = df['P_emaildomain'].str.split('.', expand=True).iloc[:,1:].fillna('').apply(lambda x:('.'.join(x)).strip('.'), axis=1).replace({'':np.nan})

for col in ['R_emaildomain_2', 'P_emaildomain_2']:
  plot_col(col, df=train)

# es has 10% fraud rate
fraud_rates = train.groupby('R_emaildomain_2')['isFraud'].mean().reset_index()
fraud_rates.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates.sort_values(by='FraudRate', ascending=False).head()

	R_emaildomain_2	FraudRate
6	es	0.100000
2	com	0.083204
3	com.mx	0.017406
7	fr	0.015015
8	net	0.009868

Two highest fraud activity domains:

• es (Spain) Category: The category associated with Spain (es) has a higher fraud rate compared to other categories (%10). This suggests that transactions originating from Spain may require closer scrutiny in terms of fraud detection.

• com (United States) Category: The category associated with the United States (com) has a significantly higher fraud rate compared to other categories (%8.32). This may imply the need for careful examination of transactions coming from this geographical region.

for col in ['R_emaildomain_1', 'P_emaildomain_1']:
  plot_col(col, df=train)

# Protonmail has 95% fraud rate
fraud_rates = train.groupby('R_emaildomain_1')['isFraud'].mean().reset_index()
fraud_rates.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates.sort_values(by='FraudRate', ascending=False).head()

	R_emaildomain_1	FraudRate
29	protonmail	0.950000
22	mail	0.361111
27	outlook	0.151882
18	icloud	0.119157
15	gmail	0.113450

Two highest fraud activity domains:

• ProtonMail Category: The ProtonMail category has a significantly higher fraud rate compared to other categories (%95). This indicates that transactions originating from ProtonMail may pose a higher risk of fraud. Careful scrutiny of transactions associated with such accounts could be crucial.

• Mail Category: This general 'mail' category exhibits a higher fraud rate compared to other categories (%36). This suggests that transactions from general email providers should be examined with caution.

# correlation between R_emaildomain_2 and P_emaildomain_2 - train
cramers_v(train.R_emaildomain_2,train.P_emaildomain_2)

0.8926752413893435

# correlation between R_emaildomain_2 and P_emaildomain_2 - test
cramers_v(test.R_emaildomain_2,test.P_emaildomain_2)

0.8813311624433039

# correlation between R_emaildomain_1 and P_emaildomain_1 - train
cramers_v(train.R_emaildomain_1,train.P_emaildomain_1)

0.6060523585208291

# correlation between R_emaildomain_1 and P_emaildomain_1 - test
cramers_v(test.R_emaildomain_1,test.P_emaildomain_1)

0.5873334446544154

# R_emaildomain_2 and P_emaildomain_2 are categorically 90% correlated. So we dropped P_emaildomain_2. (Try vice versa later)
# R_emaildomain_1, P_emaildomain_1, R_emaildomain_2 are staying
train = train.drop(['R_emaildomain','P_emaildomain','P_emaildomain_2'], axis=1)
test = test.drop(['R_emaildomain','P_emaildomain','P_emaildomain_2'], axis=1)

# Target Encoding For R_emaildomain_1, P_emaildomain_1, R_emaildomain_2 (taking into account the average of the target feature in train set)
temp_dict = train.groupby(['R_emaildomain_1'])['isFraud'].agg(['mean']).to_dict()['mean']
train['R_emaildomain_1_target_encoded'] = train['R_emaildomain_1'].replace(temp_dict)
test['R_emaildomain_1_target_encoded']  = test['R_emaildomain_1'].replace(temp_dict)


temp_dict = train.groupby(['P_emaildomain_1'])['isFraud'].agg(['mean']).to_dict()['mean']
train['P_emaildomain_1_target_encoded'] = train['P_emaildomain_1'].replace(temp_dict)
test['P_emaildomain_1_target_encoded']  = test['P_emaildomain_1'].replace(temp_dict)

temp_dict = train.groupby(['R_emaildomain_2'])['isFraud'].agg(['mean']).to_dict()['mean']
train['R_emaildomain_2_target_encoded'] = train['R_emaildomain_2'].replace(temp_dict)
test['R_emaildomain_2_target_encoded']  = test['R_emaildomain_2'].replace(temp_dict)

# List of original columns to drop
columns_to_drop = ['R_emaildomain_1', 'R_emaildomain_2', 'P_emaildomain_1']

# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)

C1 .... C14 : Counting variables such as how many addresses are found to be associated with the payment card (numeric)

column_details(regex='^C\d', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

C1:  float64, 1577, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 4.682e+03 4.684e+03 4.685e+03]

C2:  float64, 1078, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 5.625e+03 5.690e+03 5.691e+03]

C4:  float64, 1214, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 2.251e+03 2.252e+03 2.253e+03]

C5:  float64, 309, %0.0
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 275. 276. 278. 279. 280. 281.
 282. 283. 284. 285. 286. 288. 289. 290. 291. 292. 293. 294. 295. 296.
 297. 298. 299. 300. 303. 304. 307. 316. 321. 324. 328. 330. 331. 332.
 349.]

C6:  float64, 1286, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 2.251e+03 2.252e+03 2.253e+03]

C7:  float64, 1101, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 2.253e+03 2.254e+03 2.255e+03]

C8:  float64, 1218, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 3.328e+03 3.330e+03 3.331e+03]

C9:  float64, 203, %0.0
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 202. 203. 204. 205. 207. 210.]

C10:  float64, 1139, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 3.254e+03 3.256e+03 3.257e+03]

C11:  float64, 1409, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 3.186e+03 3.187e+03 3.188e+03]

C12:  float64, 1192, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 3.186e+03 3.187e+03 3.188e+03]

C13:  float64, 1585, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 2.915e+03 2.917e+03 2.918e+03]

C14:  float64, 1086, %0.0
[0.000e+00 1.000e+00 2.000e+00 ... 1.426e+03 1.428e+03 1.429e+03]

# Finding highly correlated columns to drop
columns=[col for col in train.columns if re.search('^C\d.*', col)]
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
drop_col

{'C1', 'C10', 'C11', 'C12', 'C14', 'C2', 'C4', 'C6', 'C7', 'C8', 'C9'}

# Select columns starting with 'C'
columns = [col for col in train.columns if re.search('^C\d.*', col)]

# Create a correlation heatmap using the make_corr function
make_corr(columns, train)

# Only C1 and C5 remained.
train = train.drop(drop_col, axis=1)
test = test.drop(drop_col, axis=1)

'''
# NO outlier for C1 column
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='C1', x='isFraud', data=train)
plt.axhline(5000, color='red')
plt.title('Train')

plt.subplot(122)
sns.stripplot(y='C1', x='isFraud', data=test)
plt.axhline(5000, color='red')
plt.title('Test')
'''

"\n# NO outlier for C1 column\nplt.figure(figsize=(12,8))\nplt.subplot(121)\nsns.stripplot(y='C1', x='isFraud', data=train)\nplt.axhline(5000, color='red')\nplt.title('Train')\n\nplt.subplot(122)\nsns.stripplot(y='C1', x='isFraud', data=test)\nplt.axhline(5000, color='red')\nplt.title('Test')\n"

# NO outlier for C5 column
plt.figure(figsize=(12,8))
plt.subplot(121)
sns.stripplot(y='C5', x='isFraud', data=train)
plt.axhline(400, color='red')
plt.title('Train')

plt.subplot(122)
sns.stripplot(y='C5', x='isFraud', data=test)
plt.axhline(400, color='red')
plt.title('Test')

Text(0.5, 1.0, 'Test')

D1...D15 : Timedelta variables, such as days between previous transaction (numeric)

for df in [train, test]:
  column_details(regex='^D\d.*', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

D1:  float64, 641, %0.06
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640.  nan]

D2:  float64, 641, %49.4
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640.  nan]

D3:  float64, 603, %46.54
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 572. 573. 574.
 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 589. 591. 592.
 593. 594. 596. 598. 599. 602. 603. 609. 610. 616. 634. 655. 674. 689.
 729.  nan]

D4:  float64, 748, %29.41
[-122.  -90.  -83.  -42.  -15.   -6.   -2.   -1.    0.    1.    2.    3.
    4.    5.    6.    7.    8.    9.   10.   11.   12.   13.   14.   15.
   16.   17.   18.   19.   20.   21.   22.   23.   24.   25.   26.   27.
   28.   29.   30.   31.   32.   33.   34.   35.   36.   37.   38.   39.
   40.   41.   42.   43.   44.   45.   46.   47.   48.   49.   50.   51.
   52.   53.   54.   55.   56.   57.   58.   59.   60.   61.   62.   63.
   64.   65.   66.   67.   68.   69.   70.   71.   72.   73.   74.   75.
   76.   77.   78.   79.   80.   81.   82.   83.   84.   85.   86.   87.
   88.   89.   90.   91.   92.   93.   94.   95.   96.   97.   98.   99.
  100.  101.  102.  103.  104.  105.  106.  107.  108.  109.  110.  111.
  112.  113.  114.  115.  116.  117.  118.  119.  120.  121.  122.  123.
  124.  125.  126.  127.  128.  129.  130.  131.  132.  133.  134.  135.
  136.  137.  138.  139.  140.  141.  142.  143.  144.  145.  146.  147.
  148.  149.  150.  151.  152.  153.  154.  155.  156.  157.  158.  159.
  160.  161.  162.  163.  164.  165.  166.  167.  168.  169.  170.  171.
  172.  173.  174.  175.  176.  177.  178.  179.  180.  181.  182.  183.
  184.  185.  186.  187.  188.  189.  190.  191.  192.  193.  194.  195.
  196.  197.  198.  199.  200.  201.  202.  203.  204.  205.  206.  207.
  208.  209.  210.  211.  212.  213.  214.  215.  216.  217.  218.  219.
  220.  221.  222.  223.  224.  225.  226.  227.  228.  229.  230.  231.
  232.  233.  234.  235.  236.  237.  238.  239.  240.  241.  242.  243.
  244.  245.  246.  247.  248.  249.  250.  251.  252.  253.  254.  255.
  256.  257.  258.  259.  260.  261.  262.  263.  264.  265.  266.  267.
  268.  269.  270.  271.  272.  273.  274.  275.  276.  277.  278.  279.
  280.  281.  282.  283.  284.  285.  286.  287.  288.  289.  290.  291.
  292.  293.  294.  295.  296.  297.  298.  299.  300.  301.  302.  303.
  304.  305.  306.  307.  308.  309.  310.  311.  312.  313.  314.  315.
  316.  317.  318.  319.  320.  321.  322.  323.  324.  325.  326.  327.
  328.  329.  330.  331.  332.  333.  334.  335.  336.  337.  338.  339.
  340.  341.  342.  343.  344.  345.  346.  347.  348.  349.  350.  351.
  352.  353.  354.  355.  356.  357.  358.  359.  360.  361.  362.  363.
  364.  365.  366.  367.  368.  369.  370.  371.  372.  373.  374.  375.
  376.  377.  378.  379.  380.  381.  382.  383.  384.  385.  386.  387.
  388.  389.  390.  391.  392.  393.  394.  395.  396.  397.  398.  399.
  400.  401.  402.  403.  404.  405.  406.  407.  408.  409.  410.  411.
  412.  413.  414.  415.  416.  417.  418.  419.  420.  421.  422.  423.
  424.  425.  426.  427.  428.  429.  430.  431.  432.  433.  434.  435.
  436.  437.  438.  439.  440.  441.  442.  443.  444.  445.  446.  447.
  448.  449.  450.  451.  452.  453.  454.  455.  456.  457.  458.  459.
  460.  461.  462.  463.  464.  465.  466.  467.  468.  469.  470.  471.
  472.  473.  474.  475.  476.  477.  478.  479.  480.  481.  482.  483.
  484.  485.  486.  487.  488.  489.  490.  491.  492.  493.  494.  495.
  496.  497.  498.  499.  500.  501.  502.  503.  504.  505.  506.  507.
  508.  509.  510.  511.  512.  513.  514.  515.  516.  517.  518.  519.
  520.  521.  522.  523.  524.  525.  526.  527.  528.  529.  530.  531.
  532.  533.  534.  535.  536.  537.  538.  539.  540.  541.  542.  543.
  544.  545.  546.  547.  548.  549.  550.  551.  552.  553.  554.  555.
  556.  557.  558.  559.  560.  561.  562.  563.  564.  565.  566.  567.
  568.  569.  570.  571.  572.  573.  574.  575.  576.  577.  578.  579.
  580.  581.  582.  583.  584.  585.  586.  587.  588.  589.  590.  591.
  592.  593.  594.  595.  596.  597.  598.  599.  600.  601.  602.  603.
  604.  605.  606.  607.  608.  609.  610.  611.  612.  613.  614.  615.
  616.  617.  618.  619.  620.  621.  622.  623.  624.  625.  627.  629.
  630.  631.  633.  634.  637.  638.  639.  641.  642.  643.  644.  645.
  646.  647.  648.  649.  650.  651.  653.  655.  656.  657.  658.  659.
  660.  663.  665.  666.  667.  668.  669.  670.  671.  672.  673.  674.
  676.  677.  678.  680.  682.  683.  684.  685.  686.  691.  692.  694.
  695.  696.  697.  698.  700.  702.  703.  704.  705.  706.  707.  708.
  709.  711.  712.  713.  714.  715.  716.  717.  718.  719.  720.  721.
  722.  723.  724.  725.  726.  727.  729.  730.  731.  732.  733.  734.
  735.  736.  738.  739.  741.  743.  744.  745.  747.  749.  751.  756.
  757.  761.  763.  765.  768.  771.  775.  777.  778.  783.  785.  799.
  802.  807.  812.  815.   nan]

D5:  float64, 632, %53.82
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
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 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
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 588. 589. 590. 593. 594. 595. 596. 598. 599. 600. 602. 603. 604. 606.
 609. 610. 616. 624. 640. 656. 663. 665. 674. 682. 683. 689. 705. 708.
 714. 715. 718. 720. 723. 724. 725. 726. 729. 730. 731. 733. 734. 745.
 757. 775.  nan]

D6:  float64, 768, %87.44
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 688. 689. 690. 691. 692. 693. 694. 695. 696. 698. 699. 700. 701. 702.
 703. 704. 705. 706. 707. 708. 710. 711. 712. 713. 715. 717. 718. 719.
 720. 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732. 733.
 734. 735. 736. 737. 738. 740. 741. 742. 744. 745. 749. 750. 751. 752.
 753. 754. 755. 756. 757. 758. 759. 760. 762. 763. 764. 766. 768. 769.
 770. 771. 774. 777. 778. 780. 781. 782. 784. 785. 787. 790. 791. 793.
 794. 796. 798. 801. 803. 804. 807. 809. 812. 814. 815. 821.  nan]

D8:  float64, 11122, %86.74
[0.00000000e+00 4.16660011e-02 8.33330005e-02 ... 1.54354163e+03
 1.70779163e+03            nan]

D9:  float64, 24, %86.74
[0.         0.041666   0.083333   0.125      0.166666   0.208333
 0.25       0.291666   0.33333299 0.375      0.416666   0.45833299
 0.5        0.54166597 0.58333302 0.625      0.66666597 0.70833302
 0.75       0.79166597 0.83333302 0.875      0.91666597 0.95833302
        nan]

D10:  float64, 766, %14.35
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D11:  float64, 624, %53.02
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 596. 597. 598. 599. 600. 601. 602. 603. 604. 605. 606. 607. 608. 609.
 610. 611. 612. 613. 614. 615. 617. 620.  nan]

D12:  float64, 592, %88.68
[-83.   0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.
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 576. 578. 581. 582. 583. 584. 585. 586. 587. 588. 589. 593. 594. 595.
 596. 600. 609. 610.  nan]

D13:  float64, 521, %89.48
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 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 222. 223. 224.
 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238.
 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252.
 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 266. 267.
 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281.
 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295.
 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309.
 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 321. 322. 323. 324.
 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338.
 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352.
 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366.
 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380.
 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394.
 396. 397. 398. 400. 401. 404. 405. 406. 407. 408. 409. 410. 411. 412.
 413. 415. 417. 419. 421. 424. 425. 426. 427. 428. 429. 432. 433. 434.
 435. 436. 438. 439. 442. 443. 444. 445. 446. 448. 449. 450. 451. 452.
 453. 454. 455. 456. 459. 460. 461. 462. 463. 464. 465. 467. 469. 470.
 473. 474. 476. 478. 479. 480. 481. 482. 483. 484. 486. 487. 488. 491.
 492. 493. 494. 497. 498. 500. 507. 513. 515. 517. 518. 523. 524. 530.
 533. 535. 544. 547. 557. 561. 563. 565. 566. 570. 574. 578. 588. 591.
 595. 606. 650. 653. 668. 672. 682. 688. 696. 699. 702. 706. 709. 710.
 713. 715. 717. 723. 726. 728. 729. 730. 731. 744. 745. 763. 764. 777.
 780. 791. 797.  nan]

D14:  float64, 744, %89.29
[-193.  -83.    0.    1.    2.    3.    4.    5.    6.    7.    8.    9.
   10.   11.   12.   13.   14.   15.   16.   17.   18.   19.   20.   21.
   22.   23.   24.   25.   26.   27.   28.   29.   30.   31.   32.   33.
   34.   35.   36.   37.   38.   39.   40.   41.   42.   43.   44.   45.
   46.   47.   48.   49.   50.   51.   52.   53.   54.   55.   56.   57.
   58.   59.   60.   61.   62.   63.   64.   65.   66.   67.   68.   69.
   70.   71.   72.   73.   74.   75.   76.   77.   78.   79.   80.   81.
   82.   83.   84.   85.   86.   87.   88.   89.   90.   91.   92.   93.
   94.   95.   96.   97.   98.   99.  100.  101.  102.  103.  104.  105.
  106.  107.  108.  109.  110.  111.  112.  113.  114.  115.  116.  117.
  118.  119.  120.  121.  122.  123.  124.  125.  126.  127.  128.  129.
  130.  131.  132.  133.  134.  135.  136.  137.  138.  139.  140.  141.
  142.  143.  144.  145.  146.  147.  148.  149.  150.  151.  152.  153.
  154.  155.  156.  157.  158.  159.  160.  161.  162.  163.  164.  165.
  166.  167.  168.  169.  170.  171.  172.  173.  174.  175.  176.  177.
  178.  179.  180.  181.  182.  183.  184.  185.  186.  187.  188.  189.
  190.  191.  192.  193.  194.  195.  196.  197.  198.  199.  200.  201.
  202.  203.  204.  205.  206.  207.  208.  209.  210.  211.  212.  213.
  214.  215.  216.  217.  218.  219.  220.  221.  222.  223.  224.  225.
  226.  227.  228.  229.  230.  231.  232.  233.  234.  235.  236.  237.
  238.  239.  240.  241.  242.  243.  244.  245.  246.  247.  248.  249.
  250.  251.  252.  253.  254.  255.  256.  257.  258.  259.  260.  261.
  262.  263.  264.  265.  266.  267.  268.  269.  270.  271.  272.  273.
  274.  275.  276.  277.  278.  279.  280.  281.  282.  283.  284.  285.
  286.  287.  288.  289.  290.  291.  292.  293.  294.  295.  296.  297.
  298.  299.  300.  301.  302.  303.  304.  305.  306.  307.  308.  309.
  310.  311.  312.  313.  314.  315.  316.  317.  318.  319.  320.  321.
  322.  323.  324.  325.  326.  327.  328.  329.  330.  331.  332.  333.
  334.  335.  336.  337.  338.  339.  340.  341.  342.  343.  344.  345.
  346.  347.  348.  349.  350.  351.  352.  353.  354.  355.  356.  357.
  358.  359.  360.  361.  362.  363.  364.  365.  366.  367.  368.  369.
  370.  371.  372.  373.  374.  375.  376.  377.  378.  379.  380.  381.
  382.  383.  384.  385.  386.  387.  388.  389.  390.  391.  392.  393.
  394.  395.  396.  397.  398.  399.  400.  401.  402.  403.  404.  405.
  406.  407.  408.  409.  410.  411.  412.  413.  414.  415.  416.  417.
  418.  419.  420.  421.  422.  423.  424.  425.  426.  427.  428.  429.
  430.  431.  432.  433.  434.  435.  436.  437.  438.  439.  440.  441.
  442.  443.  444.  445.  446.  447.  448.  449.  450.  451.  452.  453.
  454.  455.  456.  457.  458.  459.  460.  461.  462.  463.  464.  465.
  466.  467.  468.  469.  470.  471.  472.  473.  474.  475.  476.  477.
  478.  479.  480.  481.  482.  483.  484.  485.  486.  487.  488.  489.
  490.  491.  492.  493.  494.  495.  496.  497.  498.  499.  500.  501.
  502.  503.  504.  505.  506.  507.  508.  509.  510.  511.  512.  513.
  514.  515.  516.  517.  518.  519.  520.  521.  522.  523.  524.  525.
  526.  527.  528.  529.  530.  531.  532.  533.  534.  535.  536.  537.
  538.  539.  540.  541.  542.  543.  544.  545.  546.  547.  548.  549.
  550.  551.  552.  553.  554.  555.  557.  558.  559.  560.  561.  562.
  563.  564.  565.  566.  567.  568.  571.  572.  573.  574.  575.  576.
  577.  578.  579.  580.  581.  582.  585.  586.  587.  588.  589.  591.
  592.  593.  594.  595.  597.  598.  599.  600.  602.  603.  604.  605.
  606.  609.  610.  611.  612.  615.  616.  617.  618.  621.  622.  623.
  624.  625.  627.  629.  630.  633.  634.  635.  636.  637.  638.  639.
  642.  643.  644.  645.  646.  647.  648.  649.  650.  651.  652.  653.
  654.  655.  656.  657.  658.  659.  660.  661.  662.  663.  664.  665.
  666.  667.  668.  669.  670.  671.  672.  673.  674.  675.  676.  677.
  678.  679.  680.  681.  682.  683.  684.  686.  688.  689.  690.  691.
  692.  693.  694.  696.  697.  698.  699.  700.  701.  702.  703.  704.
  705.  706.  707.  708.  709.  711.  713.  714.  716.  718.  719.  720.
  721.  722.  723.  724.  725.  727.  728.  729.  730.  731.  732.  733.
  734.  735.  736.  739.  740.  742.  743.  744.  745.  746.  747.  749.
  750.  751.  752.  753.  756.  757.  758.  761.  762.  763.  767.  771.
  775.  781.  783.  785.  786.  787.  788.  791.  796.  799.  808.  815.
   nan]

D15:  float64, 803, %16.33
[-83. -60. -53. -30. -29. -15. -13.  -6.  -3.  -2.  -1.   0.   1.   2.
   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.  14.  15.  16.
  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.  28.  29.  30.
  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.  42.  43.  44.
  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.  56.  57.  58.
  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.  70.  71.  72.
  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.  84.  85.  86.
  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.  98.  99. 100.
 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111. 112. 113. 114.
 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125. 126. 127. 128.
 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139. 140. 141. 142.
 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153. 154. 155. 156.
 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167. 168. 169. 170.
 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181. 182. 183. 184.
 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195. 196. 197. 198.
 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209. 210. 211. 212.
 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223. 224. 225. 226.
 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237. 238. 239. 240.
 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251. 252. 253. 254.
 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265. 266. 267. 268.
 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279. 280. 281. 282.
 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293. 294. 295. 296.
 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307. 308. 309. 310.
 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321. 322. 323. 324.
 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335. 336. 337. 338.
 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349. 350. 351. 352.
 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363. 364. 365. 366.
 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377. 378. 379. 380.
 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392. 393. 394.
 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406. 407. 408.
 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420. 421. 422.
 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434. 435. 436.
 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448. 449. 450.
 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462. 463. 464.
 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475. 476. 477. 478.
 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490. 491. 492.
 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503. 504. 505. 506.
 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518. 519. 520.
 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534.
 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546. 547. 548.
 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560. 561. 562.
 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573. 574. 575. 576.
 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588. 589. 590.
 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602. 603. 604.
 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 617. 618.
 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630. 631. 632.
 633. 634. 635. 637. 638. 639. 640. 641. 642. 643. 644. 645. 646. 647.
 648. 649. 650. 651. 652. 653. 654. 655. 656. 657. 658. 659. 660. 661.
 662. 663. 664. 665. 666. 667. 668. 669. 670. 671. 672. 673. 674. 675.
 676. 677. 678. 679. 680. 682. 683. 684. 685. 686. 687. 688. 689. 690.
 691. 692. 693. 694. 695. 696. 697. 698. 699. 700. 701. 702. 703. 704.
 705. 706. 707. 708. 709. 710. 711. 712. 713. 714. 715. 716. 717. 718.
 719. 720. 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732.
 733. 734. 735. 736. 737. 738. 739. 740. 741. 742. 743. 744. 745. 749.
 750. 751. 752. 753. 754. 755. 756. 757. 758. 759. 763. 764. 765. 766.
 768. 770. 771. 772. 773. 774. 777. 778. 779. 780. 781. 782. 784. 785.
 786. 787. 788. 790. 791. 793. 796. 798. 799. 801. 802. 803. 805. 806.
 807. 812. 815. 818. 821.  nan]

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

D1:  float64, 640, %0.67
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639.  nan]

D2:  float64, 640, %42.0
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639.  nan]

D3:  float64, 628, %38.43
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 506. 507. 508. 509. 510. 511. 512. 513. 514. 516. 518. 519. 520. 521.
 522. 523. 524. 525. 526. 527. 528. 529. 530. 531. 532. 533. 534. 536.
 537. 538. 539. 541. 542. 543. 544. 546. 547. 548. 549. 551. 552. 555.
 556. 557. 558. 559. 561. 562. 563. 564. 565. 567. 568. 569. 570. 572.
 573. 574. 575. 576. 577. 580. 581. 582. 583. 584. 585. 586. 587. 588.
 589. 590. 592. 594. 595. 596. 597. 598. 600. 601. 602. 603. 605. 606.
 607. 608. 609. 610. 611. 612. 613. 614. 615. 616. 618. 619. 620. 622.
 623. 625. 627. 628. 630. 633. 636. 638. 639. 640. 641. 642. 643. 644.
 648. 649. 651. 661. 682. 691. 692. 729. 731. 749. 784. 819.  nan]

D4:  float64, 751, %26.2
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 610. 611. 612. 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623.
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 652. 653. 654. 655. 656. 657. 658. 659. 660. 661. 662. 663. 664. 665.
 666. 667. 668. 669. 670. 671. 672. 673. 675. 677. 685. 689. 692. 693.
 694. 707. 711. 713. 717. 718. 724. 726. 727. 728. 729. 730. 731. 732.
 733. 734. 736. 737. 742. 744. 746. 748. 750. 753. 756. 759. 760. 763.
 764. 766. 768. 772. 773. 774. 775. 776. 779. 784. 785. 786. 789. 798.
 800. 801. 810. 812. 815. 818. 819. 820. 822. 823. 825. 828. 829. 833.
 835. 838. 840. 843. 846. 855. 860. 864. 869.  nan]

D5:  float64, 668, %48.42
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  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
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 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
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 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 634. 635. 636. 638. 639. 640. 642. 643. 644. 645. 646.
 647. 648. 649. 650. 651. 652. 654. 655. 656. 657. 661. 667. 669. 671.
 682. 711. 716. 726. 731. 733. 736. 760. 801. 819.  nan]

D6:  float64, 722, %88.09
[-74.   0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.
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 343. 344. 345. 346. 347. 348. 349. 350. 351. 352. 353. 354. 355. 356.
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 385. 386. 387. 388. 389. 390. 391. 392. 393. 394. 395. 396. 397. 398.
 399. 400. 401. 402. 403. 404. 405. 406. 407. 408. 409. 410. 411. 412.
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 441. 442. 443. 444. 445. 447. 448. 449. 450. 451. 452. 453. 454. 455.
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 470. 471. 472. 473. 474. 475. 476. 477. 478. 479. 480. 481. 482. 483.
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 540. 541. 542. 543. 544. 545. 546. 547. 548. 549. 550. 551. 552. 553.
 554. 555. 556. 557. 558. 559. 560. 561. 562. 563. 564. 565. 566. 567.
 570. 571. 572. 573. 574. 575. 577. 578. 579. 580. 581. 583. 584. 585.
 586. 587. 588. 589. 591. 592. 594. 595. 601. 602. 604. 605. 607. 608.
 613. 615. 616. 620. 624. 625. 628. 629. 632. 635. 636. 638. 640. 642.
 645. 647. 650. 651. 652. 654. 656. 657. 660. 663. 664. 665. 666. 667.
 668. 671. 673. 674. 676. 677. 678. 679. 680. 681. 684. 685. 689. 690.
 691. 692. 693. 694. 696. 698. 699. 700. 702. 705. 707. 710. 713. 717.
 718. 724. 726. 727. 728. 729. 730. 731. 732. 733. 735. 736. 737. 738.
 740. 742. 744. 745. 746. 747. 750. 751. 752. 756. 759. 760. 763. 766.
 767. 768. 772. 775. 779. 783. 786. 787. 798. 800. 801. 803. 804. 805.
 810. 813. 815. 816. 818. 820. 823. 824. 825. 826. 828. 829. 830. 832.
 834. 835. 837. 839. 841. 842. 843. 845. 846. 847. 849. 853. 854. 856.
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D8:  float64, 5086, %89.04
[0.00000000e+00 4.16660011e-02 8.33330005e-02 ... 1.34291663e+03
 1.63887500e+03            nan]

D9:  float64, 24, %89.04
[0.         0.041666   0.083333   0.125      0.166666   0.208333
 0.25       0.291666   0.33333299 0.375      0.416666   0.45833299
 0.5        0.54166597 0.58333302 0.625      0.66666597 0.70833302
 0.75       0.79166597 0.83333302 0.875      0.91666597 0.95833302
        nan]

D10:  float64, 766, %8.44
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  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
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 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
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 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
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 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
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D11:  float64, 670, %30.12
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D12:  float64, 606, %90.11
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D13:  float64, 425, %89.6
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D14:  float64, 665, %90.02
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D15:  float64, 802, %11.38
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 613. 614. 615. 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626.
 627. 628. 629. 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640.
 641. 642. 643. 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654.
 655. 656. 657. 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668.
 669. 670. 671. 672. 674. 677. 678. 680. 681. 683. 685. 687. 689. 690.
 691. 692. 693. 694. 696. 700. 705. 707. 711. 713. 714. 715. 717. 718.
 721. 722. 723. 724. 725. 726. 727. 728. 729. 730. 731. 732. 733. 734.
 735. 736. 737. 738. 739. 740. 742. 744. 745. 746. 747. 748. 750. 751.
 752. 753. 754. 755. 756. 758. 760. 761. 763. 764. 768. 769. 770. 772.
 773. 774. 779. 784. 786. 789. 790. 791. 796. 798. 800. 801. 804. 807.
 809. 810. 811. 812. 813. 814. 815. 818. 819. 820. 823. 824. 825. 827.
 829. 830. 833. 834. 835. 836. 837. 838. 839. 840. 841. 842. 843. 844.
 845. 847. 849. 850. 851. 854. 855. 856. 857. 859. 861. 864. 865. 867.
 868. 876. 878. 879.  nan]

columns=[col for col in train.columns if re.search('^D\d.*', col)]

corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
drop_col

{'D11', 'D12', 'D2', 'D4'}

# Create a correlation heatmap using the make_corr function
make_corr(columns, train)

# The correlated columns having the most missing values are dropped. So, we replaced some columns in the dropping column list below.
drop_col={'D11', 'D12', 'D2', 'D4'}
for df in [train, test]:
  df = df.drop(drop_col, axis=1)

M1 ... M9 : Match variables, used to verify information such as names on the card and address. (nominal categoric)

column_details(regex='^M\d*', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

M1:  object, 2, %51.88
['F' 'T' nan]

M2:  object, 2, %51.88
['F' 'T' nan]

M3:  object, 2, %51.88
['F' 'T' nan]

M4:  object, 3, %48.26
['M0' 'M1' 'M2' nan]

M5:  object, 2, %60.23
['F' 'T' nan]

M6:  object, 2, %30.45
['F' 'T' nan]

M7:  object, 2, %64.81
['F' 'T' nan]

M8:  object, 2, %64.8
['F' 'T' nan]

M9:  object, 2, %64.8
['F' 'T' nan]

# Just M4 has different categories, others have F,T,nan
fig, ax = plt.subplots(3, 3, figsize=(20, 15))
fig.suptitle('Count Plots for Columns M1 to M9 in Train Data', fontsize=16)

for i, col in enumerate(['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9']):
    row_num = i // 3
    col_num = i % 3
    sns.countplot(x=col, ax=ax[row_num, col_num], hue='isFraud', data=train)
    ax[row_num, col_num].set_title(f'{col} Train', fontsize=14)

# Target Encoding for M columns
# Define the list of columns from 'M1' to 'M9'
m_columns = [f'M{i}' for i in range(1, 10)]

# Calculate and replace target mean for each column
for col in m_columns:
    temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
    train[col+'_target_encoded'] = train[col].replace(temp_dict)
    test[col+'_target_encoded'] = test[col].replace(temp_dict)

# List of original columns to drop
columns_to_drop = ['M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9']

# Drop the original columns
train.drop(columns_to_drop, axis=1, inplace=True)
test.drop(columns_to_drop, axis=1, inplace=True)

V1-V339 : Vesta-engineered features that encompass ranking, counting, and various entity relationships.(numeric)

We identified redundancy and correlation among the 'V' columns, and dropped correlated columns with a correlation coefficient (r) greater than 0.75. This process resulted in retaining only 69 independent 'V' columns.

column_details(regex='V\d*', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

V1:  float32, 2, %30.12
[ 0.  1. nan]

V2:  float32, 9, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8. nan]

V3:  float32, 10, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. nan]

V4:  float32, 7, %30.12
[ 0.  1.  2.  3.  4.  5.  6. nan]

V5:  float32, 7, %30.12
[ 0.  1.  2.  3.  4.  5.  6. nan]

V6:  float32, 10, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. nan]

V7:  float32, 10, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. nan]

V8:  float32, 9, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8. nan]

V9:  float32, 9, %30.12
[ 0.  1.  2.  3.  4.  5.  6.  7.  8. nan]

V10:  float32, 5, %30.12
[ 0.  1.  2.  3.  4. nan]

V11:  float32, 6, %30.12
[ 0.  1.  2.  3.  4.  5. nan]

V12:  float32, 4, %8.45
[ 0.  1.  2.  3. nan]

V13:  float32, 7, %8.45
[ 0.  1.  2.  3.  4.  5.  6. nan]

V14:  float32, 2, %8.45
[ 0.  1. nan]

V15:  float32, 3, %8.45
[ 0.  1.  2. nan]

V16:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V17:  float32, 10, %8.45
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. nan]

V18:  float32, 10, %8.45
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. nan]

V19:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V20:  float32, 7, %8.45
[ 0.  1.  2.  3.  4.  5.  6. nan]

V21:  float32, 4, %8.45
[ 0.  1.  2.  3. nan]

V22:  float32, 6, %8.45
[ 0.  1.  2.  3.  4.  7. nan]

V23:  float32, 9, %8.45
[ 0.  1.  2.  3.  4.  5.  6.  7.  8. nan]

V24:  float32, 9, %8.45
[ 0.  1.  2.  3.  4.  5.  6.  7.  8. nan]

V25:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V26:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V27:  float32, 2, %8.45
[ 0.  1. nan]

V28:  float32, 3, %8.45
[ 0.  1.  2. nan]

V29:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V30:  float32, 7, %8.45
[ 0.  1.  2.  3.  4.  5.  6. nan]

V31:  float32, 4, %8.45
[ 0.  1.  2.  3. nan]

V32:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V33:  float32, 3, %8.45
[ 0.  1.  2. nan]

V34:  float32, 5, %8.45
[ 0.  1.  2.  3.  4. nan]

V35:  float32, 4, %26.21
[ 0.  1.  2.  3. nan]

V36:  float32, 6, %26.21
[ 0.  1.  2.  3.  4.  5. nan]

V37:  float32, 18, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 18. 21.
 nan]

V38:  float32, 31, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. nan]

V39:  float32, 16, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. nan]

V40:  float32, 17, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 24. nan]

V41:  float32, 2, %26.21
[ 0.  1. nan]

V42:  float32, 6, %26.21
[ 0.  1.  2.  3.  4.  5. nan]

V43:  float32, 8, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V44:  float32, 17, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 15. 19. 32. nan]

V45:  float32, 22, %26.21
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 32. nan]

V46:  float32, 6, %26.21
[ 0.  1.  2.  3.  4.  5. nan]

V47:  float32, 7, %26.21
[ 0.  1.  2.  3.  4.  5.  6. nan]

V48:  float32, 4, %26.21
[ 0.  1.  2.  3. nan]

V49:  float32, 6, %26.21
[ 0.  1.  2.  3.  4.  5. nan]

V50:  float32, 5, %26.21
[ 0.  1.  2.  3.  4. nan]

V51:  float32, 6, %26.21
[ 0.  1.  2.  3.  4.  5. nan]

V52:  float32, 7, %26.21
[ 0.  1.  2.  3.  4.  5.  6. nan]

V53:  float32, 5, %8.46
[ 0.  1.  2.  3.  4. nan]

V54:  float32, 5, %8.46
[ 0.  1.  2.  3.  4. nan]

V55:  float32, 14, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. nan]

V56:  float32, 52, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35.
 36. 37. 38. 39. 40. 41. 42. 43. 44. 45. 46. 47. 48. 49. 50. 51. nan]

V57:  float32, 7, %8.46
[ 0.  1.  2.  3.  4.  5.  6. nan]

V58:  float32, 11, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. nan]

V59:  float32, 17, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. nan]

V60:  float32, 17, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. nan]

V61:  float32, 7, %8.46
[ 0.  1.  2.  3.  4.  5.  6. nan]

V62:  float32, 11, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. nan]

V63:  float32, 6, %8.46
[ 0.  1.  2.  3.  4.  5. nan]

V64:  float32, 8, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V65:  float32, 2, %8.46
[ 0.  1. nan]

V66:  float32, 8, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V67:  float32, 8, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V68:  float32, 3, %8.46
[ 0.  1.  2. nan]

V69:  float32, 5, %8.46
[ 0.  1.  2.  3.  4. nan]

V70:  float32, 6, %8.46
[ 0.  1.  2.  3.  4.  5. nan]

V71:  float32, 7, %8.46
[ 0.  1.  2.  3.  4.  5.  6. nan]

V72:  float32, 11, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. nan]

V73:  float32, 8, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V74:  float32, 8, %8.46
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V75:  float32, 4, %11.4
[ 0.  1.  2.  3. nan]

V76:  float32, 6, %11.4
[ 0.  1.  2.  3.  4.  5. nan]

V77:  float32, 19, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. nan]

V78:  float32, 32, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. nan]

V79:  float32, 8, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V80:  float32, 16, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. nan]

V81:  float32, 16, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. nan]

V82:  float32, 7, %11.4
[ 0.  1.  2.  3.  4.  5.  6. nan]

V83:  float32, 8, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V84:  float32, 7, %11.4
[ 0.  1.  2.  3.  4.  5.  7. nan]

V85:  float32, 8, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V86:  float32, 14, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. nan]

V87:  float32, 23, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12. 13. 14. 15. 16. 17.
 18. 19. 20. 21. 22. nan]

V88:  float32, 2, %11.4
[ 0.  1. nan]

V89:  float32, 3, %11.4
[ 0.  1.  2. nan]

V90:  float32, 5, %11.4
[ 0.  1.  2.  3.  4. nan]

V91:  float32, 6, %11.4
[ 0.  1.  2.  3.  4.  5. nan]

V92:  float32, 7, %11.4
[ 0.  1.  2.  3.  4.  5.  6. nan]

V93:  float32, 8, %11.4
[ 0.  1.  2.  3.  4.  5.  6.  7. nan]

V94:  float32, 3, %11.4
[ 0.  1.  2. nan]

V95:  float32, 881, %0.21
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
 238. 239. 240. 241. 242. 243. 244. 245. 246. 247. 248. 249. 250. 251.
 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643.
 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657.
 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671.
 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685.
 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699.
 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713.
 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727.
 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741.
 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755.
 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769.
 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783.
 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797.
 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811.
 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825.
 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839.
 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853.
 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867.
 868. 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880.  nan]

V96:  float32, 1410, %0.21
[0.000e+00 1.000e+00 2.000e+00 ... 1.409e+03 1.410e+03       nan]

V97:  float32, 976, %0.21
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
  14.  15.  16.  17.  18.  19.  20.  21.  22.  23.  24.  25.  26.  27.
  28.  29.  30.  31.  32.  33.  34.  35.  36.  37.  38.  39.  40.  41.
  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
  70.  71.  72.  73.  74.  75.  76.  77.  78.  79.  80.  81.  82.  83.
  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
 126. 127. 128. 129. 130. 131. 132. 133. 134. 135. 136. 137. 138. 139.
 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
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 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
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 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
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 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 372. 373. 374. 375. 376. 377. 378.
 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391. 392.
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 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448.
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 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699. 700.
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 7.0423742e+04 7.0873742e+04 7.1273742e+04 7.1373742e+04 7.1481703e+04
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 9.5231578e+04 9.6716578e+04 9.8336578e+04 1.0017561e+05 1.0332561e+05
 1.4839425e+05 1.6164430e+05           nan]

V161:  float32, 33, %90.04
[  0.     5.     8.75  10.    15.    20.    25.    27.    30.    33.
  35.    36.    40.    45.    50.    60.    65.    70.    75.    80.
  85.    92.   100.   125.   150.   200.   225.   250.   300.   325.
 400.   475.   500.      nan]

V162:  float32, 96, %90.04
[   0.      5.      8.75   10.     15.     20.     25.     27.     30.
   33.     35.     36.     40.     41.     45.     48.     50.     55.
   60.     65.     66.     70.     73.     75.     80.     85.     90.
   95.    100.    105.    113.    115.    120.    125.    130.    135.
  140.    145.    150.    155.    160.    163.    165.    168.    175.
  180.    188.    190.    191.    195.    200.    203.    216.    218.
  220.    225.    250.    255.    256.    266.    270.    275.    300.
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  420.    425.    470.    475.    485.    500.    515.    525.    540.
  545.    570.    620.    660.    670.    720.    745.    760.    795.
  870.    945.    995.   1045.   1120.   1140.       nan]

V163:  float32, 45, %90.04
[  0.     5.     8.75  10.    15.    20.    25.    27.    30.    33.
  35.    36.    40.    41.    45.    48.    50.    55.    60.    65.
  70.    73.    75.    80.    85.    90.    95.   100.   105.   125.
 130.   140.   150.   165.   175.   200.   225.   250.   255.   300.
 325.   350.   400.   475.   500.      nan]

V164:  float32, 1414, %90.04
[0.0000e+00 5.0000e+00 1.0000e+01 ... 9.3630e+04 9.3736e+04        nan]

V165:  float32, 1506, %90.04
[0.0000e+00 5.0000e+00 1.0000e+01 ... 9.8420e+04 9.8476e+04        nan]

V166:  float32, 227, %90.04
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 1.0760e+03 1.1000e+03 1.1250e+03 1.1270e+03 1.1650e+03 1.1660e+03
 1.1700e+03 1.1780e+03 1.1820e+03 1.2000e+03 1.2250e+03 1.2330e+03
 1.2370e+03 1.2500e+03 1.2880e+03 1.3000e+03 1.3390e+03 1.3470e+03
 1.3500e+03 1.3750e+03 1.3980e+03 1.4000e+03 1.4250e+03 1.4450e+03
 1.4490e+03 1.4500e+03 1.4570e+03 1.5000e+03 1.5040e+03 1.5120e+03
 1.5510e+03 1.6000e+03 1.7500e+03 1.7750e+03 1.8000e+03 1.8250e+03
 1.9000e+03 2.0000e+03 2.2000e+03 2.2500e+03 2.4000e+03 2.5000e+03
 3.0000e+03 3.1000e+03 4.0000e+03 4.2500e+03 4.4000e+03 4.5000e+03
 5.0000e+03 5.5000e+03 5.6000e+03 6.0000e+03 6.7500e+03 7.5000e+03
 8.0000e+03 9.0000e+03 9.4000e+03 1.0000e+04 1.4100e+04 2.9400e+04
 3.0250e+04 5.2030e+04 6.0500e+04 9.0750e+04 1.0406e+05        nan]

V167:  float32, 873, %80.85
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.  11.  12.  13.
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  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
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  84.  85.  86.  87.  88.  89.  90.  91.  92.  93.  94.  95.  96.  97.
  98.  99. 100. 101. 102. 103. 104. 105. 106. 107. 108. 109. 110. 111.
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 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643.
 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657.
 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671.
 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685.
 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699.
 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713.
 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727.
 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741.
 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755.
 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769.
 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783.
 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797.
 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811.
 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825.
 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839.
 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853.
 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867.
 868. 869. 870. 871. 872.  nan]

V168:  float32, 965, %80.85
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  42.  43.  44.  45.  46.  47.  48.  49.  50.  51.  52.  53.  54.  55.
  56.  57.  58.  59.  60.  61.  62.  63.  64.  65.  66.  67.  68.  69.
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 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
 224. 225. 226. 227. 228. 229. 230. 231. 232. 233. 234. 235. 236. 237.
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 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
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 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
 294. 295. 296. 297. 298. 299. 300. 301. 302. 303. 304. 305. 306. 307.
 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 372. 373. 374. 375. 376. 377.
 378. 379. 380. 381. 382. 383. 384. 385. 386. 387. 388. 389. 390. 391.
 392. 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405.
 406. 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419.
 420. 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433.
 434. 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447.
 448. 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461.
 462. 463. 464. 465. 466. 467. 468. 469. 470. 471. 472. 473. 474. 475.
 476. 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489.
 490. 491. 492. 493. 494. 495. 496. 497. 498. 499. 500. 501. 502. 503.
 504. 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517.
 518. 519. 520. 521. 522. 523. 524. 525. 526. 527. 528. 529. 530. 531.
 532. 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545.
 546. 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559.
 560. 561. 562. 563. 564. 565. 566. 567. 568. 569. 570. 571. 572. 573.
 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
 616. 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629.
 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643.
 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657.
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 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699.
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 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741.
 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755.
 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769.
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V206:  float32, 440, %80.85
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V208:  float32, 783, %80.77
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   3.2627   3.2754   3.3146   3.4063   3.5828   3.8732   3.9347   4.0746
   4.1806   4.1976   4.5495   4.6725   4.7509   4.7954   4.8336   4.8569
   5.       5.0414   5.2873   5.3048   5.3488   5.4717   5.8406   5.8427
   6.042    6.0865   6.2095   6.3324   6.36     6.3939   6.4554   6.5169
   6.5254   6.6398   6.6462   6.7066   6.7628   6.8863   7.       7.0087
   7.1317   7.1932   7.3161   7.3718   7.3776   7.5011   7.5525   7.6235
   7.6405   7.6574   7.685    7.7465   7.8694   7.9754   7.9929   8.
   8.0539   8.1154   8.2383   8.2775   8.2998   8.3612   8.3613   8.6072
   8.6687   8.7514   8.9146   8.9761   9.0376   9.099    9.1605   9.1775
   9.222    9.2835   9.3004   9.345    9.4679   9.5294   9.5464   9.5909
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  14.8029  14.8167  14.8321  14.8787  14.9396  15.      15.0016  15.105
  15.1055  15.2258  15.247   15.37    15.432   15.4681  15.5279  15.5544
  15.6159  15.6774  15.7389  15.8618  15.9     15.9233  15.9509  15.9853
  16.1078  16.2535  16.3134  16.3706  16.4766  16.5996  16.6759  16.7226
  16.801   16.8572  16.8921  16.977   17.0914  17.1529  17.153   17.2144
  17.2759  17.2807  17.3374  17.4603  17.5218  17.7031  17.7062  17.8292
  17.9522  17.9829  18.      18.1981  18.321   18.3825  18.444   18.567
  18.6094  18.6899  18.8128  18.8744  18.9358  19.0588  19.1203  19.1818
  19.2432  19.3047  19.3344  19.3662  19.4277  19.6121  19.6365  19.6736
  19.7966  19.9195  20.      20.0425  20.1199  20.1654  20.2884  20.4114
  20.4728  20.5343  20.5958  20.6573  20.6636  20.7802  20.7845  20.8417
  20.899   20.9032  20.9647  21.0262  21.0569  21.1237  21.2106  21.2678
  21.2721  21.395   21.4565  21.518   21.641   21.7024  21.7639  21.8869
  22.0098  22.1328  22.1943  22.3172  22.3787  22.4402  22.5017  22.5632
  22.6246  22.6861  22.7179  22.7476  22.8706  22.9935  23.0804  23.1165
  23.2394  23.3624  23.4854  23.5469  23.6083  23.7928  23.8209  23.8542
  24.0387  24.0472  24.1002  24.168   24.2231  24.3461  24.4076  24.469
  24.5305  24.592   24.715   24.8379  24.8994  24.9609  25.      25.0838
  25.2068  25.2683  25.3764  25.3912  25.4527  25.5142  25.5757  25.6785
  25.6986  25.7389  25.7601  25.8216  25.8428  25.8831  25.9446  25.9806
  26.0675  26.129   26.1905  26.252   26.4051  26.4364  26.464   26.5594
  26.5848  26.6823  26.8053  26.9282  27.0512  27.1127  27.189   27.1916
  27.2494  27.2717  27.2971  27.4816  27.666   27.6724  27.7895  27.7932
  27.8504  27.9119  28.0348  28.0349  28.1578  28.2199  28.2808  28.3974
  28.4658  28.5882  28.8961  29.0021  29.0026  29.1415  29.2645  29.3265
  29.4489  29.5104  29.5274  29.5719  29.6122  29.7563  29.7569  29.7648
  29.8178  29.8793  30.      30.0028  30.0032  30.1252  30.1496  30.1867
  30.21    30.2651  30.3096  30.3711  30.4941  30.5556  30.5725  30.6176
  30.74    30.8142  30.8635  30.9244  30.9859  31.0474  31.1089  31.1799
  31.2318  31.2371  31.3548  31.4163  31.4184  31.4778  31.6007  31.6601
  31.8466  31.9018  31.9701  32.0311  32.1434  32.1917  32.2155  32.2643
  32.3385  32.3894  32.4614  32.5844  32.7476  32.8308  32.9289  33.
  33.0148  33.1102  33.1992  33.2607  33.5071  33.5336  33.5395  33.6916
  33.814   33.8755  33.9375  34.0599  34.1829  34.3058  34.4288  34.5518
  34.6133  34.6747  34.7977  34.8592  34.9206  35.      35.228   35.2896
  35.351   35.4125  35.4496  35.5354  35.6584  35.7082  35.9658  36.0273
  36.2732  36.3347  36.5191  36.5806  36.6145  36.6421  36.7036  36.765
  36.888   36.977   37.011   37.2442  37.3798  37.4413  37.5643  38.0561
  38.1176  38.1791  38.3635  38.7324  38.7939  38.8512  39.1522  39.2242
  39.2857  39.3472  39.4087  39.5316  39.5931  39.7161  39.839   39.962
  40.      40.085   40.2079  40.2694  40.3309  40.4538  40.5768  40.6022
  40.6998  40.7612  40.8227  40.9075  40.9456  41.0072  41.3146  41.376
  41.569   41.6834  42.0523  42.1138  42.2368  42.4212  42.6671  42.7774
  42.913   43.      43.036   43.159   43.2586  43.5278  43.6508  43.8967
  44.0812  44.1119  44.1426  44.2042  44.2656  44.4691  44.5115  44.6345
  44.8189  44.8762  45.      45.0034  45.2493  45.4337  45.4952  45.6171
  45.6182  45.6775  45.6796  45.7411  46.      46.11    46.233   46.2817
  46.4174  46.4788  46.6018  46.7248  46.7651  46.9707  47.1552  47.4011
  47.7085  47.8929  48.2003  48.4462  48.4568  48.6922  49.1501  49.184
  49.4299  49.6144  50.      50.1677  50.4136  50.5366  50.721   50.9054
  51.3973  51.4588  51.6432  52.0121  52.0736  52.135   52.258   52.9343
  53.1187  53.4876  54.1363  54.2254  54.7177  55.      55.0866  55.103
  55.332   55.394   55.5164  56.1927  56.2547  56.3114  56.3157  56.4392
  56.6846  57.3608  57.3932  57.4223  57.5453  57.6682  57.6688  57.7917
  58.283   58.7283  59.0208  59.5132  59.6356  60.      60.066   60.4348
  60.6193  60.7422  60.8037  60.9548  61.1726  61.357   61.603   61.7264
  62.2474  62.4637  62.495   62.6481  62.8326  63.079   63.2014  63.4473
  63.5703  63.9397  64.431   64.7384  65.      65.0458  65.0628  65.1688
  65.5059  65.6612  65.9792  66.3984  67.0137  67.0667  67.505   67.8744
  67.9969  68.1198  68.6117  68.6732  68.9806  69.227   69.4724  70.
  70.1487  70.4561  70.702   70.9479  71.0708  71.0709  71.1938  71.9316
  73.1612  73.229   73.2842  73.5306  73.776   73.8767  73.9329  74.8
  75.      75.2515  75.4042  76.1737  76.4202  76.6041  77.1574  77.3418
  78.0796  78.5714  78.5958  78.8174  79.0633  79.1502  79.7544  79.8625
  80.      80.1699  80.4794  80.8419  80.9077  81.1536  81.2766  81.6454
  82.2475  82.8326  84.4608  84.4735  84.5965  85.      85.0883  85.5802
  85.8261  86.3179  86.5024  86.5638  87.0557  87.1256  87.768   88.1623
  88.2238  88.3775  89.6378  89.7608  90.1127  90.4986  92.22    92.8348
  94.0644  94.8022  95.      96.6466 100.     100.8277 100.9501 103.2864
 104.393  104.516  104.7683 104.7689 105.     105.2845 107.713  108.4507
 108.5737 110.     110.2336 110.664  115.152  115.8283 117.7538 118.2875
 120.     120.2549 122.2222 124.1896 124.3126 125.     125.911  126.8333
 127.3866 130.2146 130.8294 131.5672 133.719  139.2522 139.4366 139.5596
 140.7277 141.1581 141.404  142.0803 142.3877 144.9698 148.6586 150.
 153.3926 153.9655 156.9584 159.4473 170.     170.2636 173.8654 183.3747
 185.6696 189.7273 192.0148 200.     212.106  212.4749 221.5745 225.
 230.1811 250.     251.0848 256.6175 300.     325.     389.7832 400.
 434.8889 475.     500.     600.     675.     700.     725.          nan]

V209:  float32, 1088, %80.77
[0.0000e+00 1.7384e+00 2.1078e+00 ... 2.4750e+03 2.6000e+03        nan]

V210:  float32, 880, %80.77
[  0.       1.7384   2.1078   2.4592   2.6807   2.7051   3.0125   3.2627
   3.2754   3.3146   3.4063   3.5828   3.8732   3.9347   4.0746   4.1806
   4.1976   4.5495   4.6725   4.7509   4.7954   4.8336   4.8569   5.
   5.0414   5.2873   5.3048   5.3488   5.4717   5.8406   5.8427   6.042
   6.0865   6.2095   6.2254   6.3324   6.36     6.3939   6.4554   6.5169
   6.5254   6.6398   6.6462   6.7066   6.7628   6.8863   7.       7.0087
   7.1317   7.1932   7.3161   7.3718   7.3776   7.5011   7.5525   7.6235
   7.6405   7.6574   7.685    7.7465   7.8694   7.9754   7.9929   8.
   8.0539   8.1154   8.2383   8.2775   8.2998   8.3612   8.3613   8.6072
   8.6687   8.7514   8.9146   8.9761   9.0376   9.099    9.1605   9.1775
   9.222    9.2835   9.3004   9.345    9.4679   9.5294   9.5464   9.5909
   9.6524   9.6672   9.7138   9.7753   9.8368   9.8485  10.      10.0827
  10.1442  10.2057  10.2248  10.2714  10.5131  10.5746  10.5921  10.6
  10.6975  10.8205  10.9434  11.0049  11.0219  11.0664  11.1279  11.1894
  11.2508  11.2985  11.3738  11.4353  11.5582  11.6812  11.7427  11.8041
  11.8656  12.      12.0501  12.173   12.2048  12.2345  12.3575  12.5419
  12.6034  12.6649  12.7264  12.7486  12.8218  12.9723  13.0338  13.0507
  13.0952  13.2182  13.2797  13.3412  13.4026  13.4641  13.5256  13.5871
  13.5945  13.6358  13.6486  13.71    13.7721  13.7726  13.8902  13.895
  14.0174  14.2019  14.2634  14.3863  14.4483  14.5093  14.6322  14.6492
  14.6937  14.7197  14.7552  14.8167  14.8321  14.8787  14.9396  15.
  15.0011  15.0016  15.105   15.1055  15.247   15.37    15.432   15.4681
  15.5279  15.5544  15.6159  15.6774  15.7092  15.7389  15.8618  15.9
  15.9233  15.9509  15.9853  16.1078  16.3134  16.3706  16.4766  16.5996
  16.6759  16.7226  16.801   16.8572  16.8921  16.977   17.0914  17.1529
  17.153   17.2144  17.2759  17.2807  17.3374  17.4603  17.5218  17.7031
  17.7062  17.8292  17.9522  17.9829  18.      18.1981  18.321   18.3825
  18.444   18.567   18.6094  18.6899  18.8128  18.8744  18.9358  19.0588
  19.1203  19.1818  19.2432  19.3047  19.3662  19.4277  19.538   19.6121
  19.6365  19.6736  19.7966  19.9195  20.      20.0425  20.1654  20.2884
  20.4114  20.4728  20.5343  20.5958  20.6573  20.6636  20.7802  20.7845
  20.8417  20.899   20.9032  20.9647  21.      21.0262  21.0569  21.1237
  21.1492  21.2106  21.2117  21.2678  21.2721  21.395   21.4565  21.518
  21.641   21.7024  21.7639  21.8254  21.8869  22.0098  22.029   22.1328
  22.1943  22.3172  22.3787  22.4402  22.5017  22.5632  22.5801  22.6246
  22.6861  22.7179  22.7476  22.8706  22.9935  23.0804  23.1165  23.2394
  23.2395  23.3624  23.4854  23.5469  23.6083  23.7928  23.8209  23.8542
  24.0387  24.0472  24.1002  24.168   24.2231  24.2846  24.3461  24.4076
  24.469   24.5305  24.592   24.715   24.8379  24.8994  24.9609  25.
  25.0838  25.1453  25.2068  25.2683  25.3764  25.3912  25.4527  25.5142
  25.5757  25.6986  25.7601  25.8216  25.8428  25.8831  25.9446  25.9806
  26.      26.0675  26.129   26.1905  26.252   26.4051  26.4364  26.464
  26.5594  26.5848  26.6823  26.8053  26.9282  27.0512  27.1127  27.1916
  27.2717  27.2971  27.3062  27.4816  27.543   27.666   27.6724  27.7895
  27.7932  27.8504  27.9119  28.0348  28.0349  28.1578  28.2199  28.2766
  28.2808  28.3974  28.4658  28.5882  28.8961  29.0021  29.0026  29.1415
  29.2645  29.3265  29.4489  29.5104  29.5274  29.5719  29.6122  29.7563
  29.7569  29.7648  29.8178  29.8793  30.      30.0028  30.0032  30.1252
  30.1496  30.1867  30.21    30.2651  30.3096  30.3097  30.3711  30.4516
  30.4517  30.4941  30.5556  30.5725  30.6176  30.74    30.8142  30.8635
  30.9244  30.9859  31.0474  31.1089  31.1799  31.2318  31.2371  31.2933
  31.3548  31.4163  31.4184  31.4778  31.6007  31.6601  31.8466  31.9018
  31.9701  32.0311  32.1434  32.1917  32.2155  32.2643  32.3385  32.3894
  32.4614  32.5844  32.7074  32.7476  32.7863  32.8308  32.9289  33.
  33.0148  33.1102  33.1992  33.2607  33.5071  33.5331  33.5395  33.6916
  33.814   33.8755  33.9375  34.0599  34.1829  34.3058  34.4288  34.5518
  34.6132  34.6133  34.6747  34.7977  34.8592  34.9206  35.      35.228
  35.2896  35.351   35.4125  35.4496  35.5354  35.6584  35.7082  35.9658
  36.      36.0273  36.2732  36.3347  36.4576  36.5191  36.5806  36.6145
  36.6421  36.7036  36.765   36.888   36.977   37.011   37.2442  37.3798
  37.4413  37.5643  38.0561  38.1176  38.1791  38.302   38.3635  38.4865
  38.7324  38.7939  39.2242  39.2857  39.326   39.3472  39.4087  39.5316
  39.5931  39.7161  39.839   39.962   40.      40.085   40.2079  40.2694
  40.3309  40.4538  40.5768  40.6022  40.6998  40.7612  40.8227  40.9075
  40.9456  41.0072  41.3146  41.376   41.569   41.6834  42.0523  42.1138
  42.2368  42.2982  42.4212  42.6671  42.7774  42.913   43.036   43.159
  43.2586  43.5278  43.6508  43.8967  44.0812  44.1119  44.1426  44.2042
  44.2656  44.2884  44.4691  44.5115  44.6345  44.8189  44.8762  45.
  45.0034  45.2493  45.4337  45.4952  45.6171  45.6182  45.6775  45.6796
  45.7411  46.      46.11    46.1715  46.233   46.2817  46.4174  46.4788
  46.6018  46.6442  46.7248  46.8864  46.9707  47.1552  47.2166  47.4011
  47.4625  47.5861  47.7085  47.8929  48.      48.2003  48.3233  48.4462
  48.4568  48.6922  48.9402  49.1501  49.184   49.3028  49.4299  49.6144
  50.      50.1677  50.4136  50.5366  50.721   50.9054  51.357   51.3973
  51.4588  51.6432  52.0121  52.0318  52.0736  52.135   52.258   52.9343
  53.1187  53.4097  53.4876  53.795   54.1363  54.2254  54.378   54.7177
  55.      55.0866  55.103   55.332   55.394   55.5164  55.6394  56.1927
  56.2547  56.3114  56.3157  56.4392  56.5531  56.6846  57.3608  57.3932
  57.4223  57.5453  57.6682  57.6688  57.7917  57.8527  58.283   58.7282
  58.7283  59.0208  59.3897  59.5132  59.6356  60.      60.066   60.4348
  60.6193  60.7422  60.8037  60.9548  61.1726  61.357   61.603   61.7264
  62.0333  62.2474  62.4637  62.495   62.5358  62.6481  62.8326  63.
  63.079   63.2014  63.4473  63.5703  63.8565  63.9397  64.431   64.7384
  65.      65.0458  65.0628  65.1688  65.2536  65.5059  65.6612  65.9681
  65.9792  66.2755  66.3984  66.6443  67.0137  67.0667  67.0672  67.1362
  67.505   67.5055  67.8744  67.9969  68.1198  68.4802  68.6117  68.6732
  68.9806  69.227   69.4724  70.      70.1487  70.4561  70.702   70.9479
  71.0708  71.0709  71.1938  71.9316  73.      73.1612  73.229   73.2842
  73.5306  73.776   73.8767  73.9329  74.5137  74.8826  75.      75.2515
  75.4042  76.1737  76.2352  76.4202  76.6041  76.727   77.1574  77.3418
  78.0796  78.5714  78.5958  78.8174  79.0633  79.1502  79.1863  79.7544
  79.8625  80.      80.1699  80.4794  80.8419  80.9077  81.1536  81.2766
  81.5225  81.6274  81.6454  82.2475  82.8326  84.4608  84.4735  84.5965
  85.      85.0883  85.5759  85.5802  85.8261  86.3179  86.5024  86.5638
  87.0557  87.1256  87.768   88.1623  88.2238  88.3775  89.6378  89.7608
  89.8319  90.      90.1127  92.22    92.8348  94.0644  94.1874  94.8022
  95.      96.4006  96.6466 100.     100.8277 100.9501 103.226  103.2864
 104.393  104.516  104.7683 104.7689 105.     105.2845 106.115  107.713
 108.4507 108.5737 110.     110.2336 110.664  113.3076 114.1938 115.
 115.152  115.8283 117.7538 118.2875 120.     120.2549 122.2222 124.1896
 124.3126 125.     125.911  126.2371 126.8333 127.3866 127.694  128.3151
 128.4937 130.     130.2146 130.4606 130.8294 131.5672 131.8132 133.5346
 133.719  135.01   139.2522 139.4366 139.5596 140.     140.7277 141.1581
 141.404  142.0803 142.3877 144.9698 145.     145.0928 148.6586 150.
 153.3926 153.9655 156.9584 159.4473 160.     165.3812 170.     170.2636
 173.8654 176.755  180.     183.3747 185.6696 189.4198 189.7273 190.
 192.0148 195.     200.     208.2942 212.106  212.4749 215.5182 221.5745
 225.     230.1811 240.     250.     251.0848 256.6175 260.     270.
 275.     300.     325.     335.     350.     389.7832 390.     400.
 434.8889 465.     475.     500.     600.     675.     700.     725.
      nan]

V211:  float32, 3017, %80.85
[0.0000e+00 8.7820e-01 1.0006e+00 ... 9.2782e+04 9.2888e+04        nan]

V212:  float32, 3377, %80.85
[0.00000e+00 8.78200e-01 1.00060e+00 ... 1.28950e+05 1.29006e+05
         nan]

V213:  float32, 3208, %80.85
[0.0000e+00 8.7820e-01 1.0006e+00 ... 9.7572e+04 9.7628e+04        nan]

V214:  float32, 732, %80.85
[0.000000e+00 1.308600e+00 2.151800e+00 2.291700e+00 2.336200e+00
 2.582200e+00 3.012500e+00 3.091000e+00 3.582800e+00 3.811800e+00
 3.890700e+00 3.934700e+00 4.197600e+00 4.488000e+00 4.611000e+00
 4.795400e+00 5.000000e+00 5.348800e+00 5.471700e+00 5.656200e+00
 6.000000e+00 6.393900e+00 6.516900e+00 6.578400e+00 6.762800e+00
 7.000000e+00 7.131700e+00 7.165600e+00 7.193200e+00 7.377600e+00
 7.412600e+00 7.623500e+00 7.685000e+00 7.992900e+00 8.000000e+00
 8.053900e+00 8.299800e+00 8.344300e+00 8.361300e+00 8.484800e+00
 8.607200e+00 8.668700e+00 8.730200e+00 8.791600e+00 8.808600e+00
 8.914600e+00 8.993000e+00 9.000000e+00 9.099000e+00 9.153100e+00
 9.283500e+00 9.467900e+00 9.529400e+00 9.590900e+00 9.775300e+00
 9.898300e+00 1.000000e+01 1.014420e+01 1.045160e+01 1.069760e+01
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          nan]

V269:  float32, 57, %82.95
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V270:  float32, 644, %80.73
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   4.7509   4.7954   4.8336   5.       5.1813   5.3      5.3048   5.3488
   5.4717   5.7346   5.8406   5.9805   6.       6.042    6.148    6.3441
   6.3939   6.4554   6.5169   6.6462   6.7628   7.       7.0087   7.1301
   7.1317   7.2546   7.3161   7.5      7.5011   7.6235   7.6405   7.685
   7.7465   7.8694   7.95     7.9781   7.9929   8.       8.0539   8.2383
   8.2998   8.3613   8.6072   8.6687   8.7302   8.75     8.8531   8.9146
   9.0376   9.099    9.1605   9.2835   9.3004   9.345    9.4679   9.5294
   9.5909   9.7753   9.8368   9.8983   9.9089  10.      10.0212  10.0901
  10.2057  10.4516  10.5131  10.6     10.6975  11.0219  11.0664  11.1894
  11.2508  11.4353  11.6812  11.7427  11.8656  12.0501  12.1116  12.173
  12.3638  12.5419  12.5928  12.6649  12.7264  12.7878  12.8493  13.0338
  13.25    13.2924  13.4026  13.4132  13.4641  13.5256  13.5457  13.5961
  13.6358  13.833   13.8902  14.0174  14.0779  14.1383  14.1404  14.2019
  14.2634  14.3248  14.3863  14.4483  14.5093  14.6216  14.6322  14.7552
  14.8787  15.      15.0016  15.0181  15.1241  15.247   15.493   15.6159
  15.6774  15.7092  15.7389  15.8618  15.8905  15.9     15.9853  16.1078
  16.2307  16.2922  16.3537  16.3706  16.4766  16.5551  16.7226  16.784
  16.801   16.8667  16.8921  16.969   17.0914  17.1529  17.153   17.2144
  17.3374  17.3379  17.4603  17.5     17.5218  17.5833  17.6448  17.7062
  17.8292  17.9522  18.1981  18.321   18.444   18.567   18.8128  18.8129
  18.9358  19.0588  19.1203  19.2432  19.274   19.4277  19.5506  19.6121
  19.7966  19.9195  19.999   20.      20.0425  20.0594  20.1654  20.2884
  20.3499  20.3933  20.4114  20.4495  20.4728  20.5343  20.5958  20.6573
  20.7188  20.8417  20.9032  21.0569  21.1237  21.1491  21.2     21.2106
  21.518   21.7024  21.7639  21.8869  22.1328  22.1943  22.3172  22.3787
  22.4402  22.6246  22.6861  22.7476  22.9935  22.9983  23.1165  23.2013
  23.2394  23.4557  23.4854  23.6698  23.7016  23.7928  23.8209  23.8542
  23.9157  24.      24.0387  24.1002  24.168   24.2231  24.3461  24.4076
  24.4097  24.592   24.715   24.7722  24.8379  24.9609  25.      25.0027
  25.0838  25.2683  25.3298  25.3912  25.3913  25.4527  25.5142  25.5757
  25.6986  25.7601  25.8216  25.8831  26.      26.0675  26.129   26.1905
  26.252   26.3134  26.3432  26.4364  26.5594  26.8053  26.9282  27.
  27.0512  27.1127  27.1916  27.2717  27.4201  27.6045  27.666   27.9119
  28.1578  28.2199  28.2766  28.4658  28.6496  28.6497  28.7726  28.8341
  28.8961  29.0021  29.1415  29.203   29.2645  29.4489  29.5104  29.5719
  29.6122  29.7563  29.7569  29.8178  29.8793  30.      30.0022  30.0028
  30.0637  30.1252  30.2482  30.3096  30.3711  30.5556  30.6176  30.9244
  30.9859  31.0474  31.1089  31.2318  31.2939  31.3548  31.4163  31.6601
  31.8     31.9701  32.0311  32.2155  32.3385  32.3745  32.3894  32.4614
  32.5844  32.8308  33.0768  33.5071  33.5395  33.5681  33.6916  33.8755
  33.9375  34.3058  34.6133  34.6747  34.9206  35.      35.228   35.2896
  35.351   35.4496  35.5354  35.5874  35.9043  35.9658  36.0273  36.1312
  36.5191  36.5806  36.6421  36.765   37.011   37.2442  37.2569  37.3798
  37.4413  37.5     37.5028  37.5643  38.0561  38.1176  38.1791  38.6094
  39.0398  39.1352  39.2242  39.3472  39.4087  39.5316  39.5932  39.839
  39.962   40.      40.085   40.2694  40.3309  40.4538  40.6022  40.6998
  40.7612  40.8227  40.9075  40.9456  41.0072  41.0686  41.376   41.6834
  41.8106  42.1732  42.2368  42.2982  42.4212  42.6374  42.6671  42.7392
  43.036   43.159   43.8967  44.      44.0812  44.1426  44.2656  44.3886
  44.45    44.4691  44.5115  44.6345  44.8762  45.      45.2493  45.4337
  45.4952  45.6182  46.233   46.4174  46.4788  46.6018  46.7248  47.1552
  47.4011  47.5855  47.6645  47.7085  48.0774  48.4462  48.8448  49.1501
  49.3027  49.4299  49.6144  49.7373  49.7988  49.9218  50.      50.1677
  50.2906  50.4136  50.5366  51.4588  51.5202  51.6432  52.0736  52.135
  52.1965  52.258   52.381   52.8728  52.9343  53.8565  54.7177  55.
  55.0866  55.394   55.5164  56.1927  56.2547  56.3157  56.5001  56.6846
  56.8695  57.3608  57.4223  57.6688  57.7917  58.124   58.529   59.0208
  59.5132  59.6356  59.7331  60.      60.066   60.4348  60.7428  60.8037
  61.357   61.7264  62.2474  62.4637  62.7096  63.079   63.2014  63.8777
  63.9397  64.431   65.      66.3984  66.5214  67.505   67.9969  68.1203
  68.6732  68.9806  69.121   69.4724  70.      70.1487  70.4561  70.6914
  70.702   70.9479  71.0094  71.0709  71.5627  71.8086  71.9316  72.6694
  73.4686  73.5306  73.776   73.8767  74.5138  74.8     75.      75.1625
  75.2515  76.1122  76.1319  76.2352  76.4202  76.4811  76.6041  77.3418
  77.4648  78.0796  78.5714  78.5958  78.6668  78.8174  79.6781  80.
  80.1699  80.9077  81.4462  81.7684  81.8914  82.1373  82.2475  83.
  83.6213  85.      85.8261  86.3179  86.5638  87.0557  87.768   88.9616
  89.6378  90.      90.2526  90.4487  91.6052  91.8172  92.      92.22
  92.8348  93.8185  94.0644  94.1874  96.893  100.     100.9501 101.442
 104.2245 104.516  105.     106.4834 106.9466 108.4507 108.5737 110.
 110.2336 110.664  110.788  112.8778 115.152  117.7538 118.2875 120.
 120.2549 121.3234 122.2222 124.1896 124.3126 124.7069 125.     125.911
 126.8333 130.645  130.8294 131.6287 133.719  134.0269 135.     139.2522
 139.4366 139.5596 141.1581 141.404  143.0025 143.3162 150.     153.3926
 153.6396 153.9655 156.9584 157.4545 158.3004 159.4473 170.     173.8654
 185.6696 189.7273 199.98   200.     202.5734 202.884  215.2415 241.
 244.5    249.     250.     252.068  300.     325.     338.5    343.25
 350.     368.5    400.     413.     423.     434.8889 475.     478.
 500.     575.     605.5    686.          nan]

V271:  float32, 776, %80.73
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 1.600000e+03          nan]

V272:  float32, 682, %80.73
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V273:  float32, 2180, %82.95
[0.0000e+00 8.7820e-01 1.0006e+00 ... 2.9887e+04 2.9997e+04        nan]

V274:  float32, 2507, %82.95
[0.0000e+00 8.7820e-01 1.0006e+00 ... 3.3500e+04 3.3606e+04        nan]

V275:  float32, 2358, %82.95
[0.0000e+00 8.7820e-01 1.0006e+00 ... 3.3075e+04 3.3181e+04        nan]

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V278:  float32, 735, %82.95
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 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880. 881. 882.
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 574. 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587.
 588. 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601.
 602. 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615.
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 630. 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643.
 644. 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657.
 658. 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671.
 672. 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685.
 686. 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699.
 700. 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713.
 714. 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727.
 728. 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741.
 742. 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755.
 756. 757. 758. 759. 760. 761. 762. 763. 764. 765. 766. 767. 768. 769.
 770. 771. 772. 773. 774. 775. 776. 777. 778. 779. 780. 781. 782. 783.
 784. 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797.
 798. 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811.
 812. 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825.
 826. 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839.
 840. 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853.
 854. 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867.
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V323:  float32, 1411, %89.98
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V324:  float32, 976, %89.98
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 112. 113. 114. 115. 116. 117. 118. 119. 120. 121. 122. 123. 124. 125.
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 140. 141. 142. 143. 144. 145. 146. 147. 148. 149. 150. 151. 152. 153.
 154. 155. 156. 157. 158. 159. 160. 161. 162. 163. 164. 165. 166. 167.
 168. 169. 170. 171. 172. 173. 174. 175. 176. 177. 178. 179. 180. 181.
 182. 183. 184. 185. 186. 187. 188. 189. 190. 191. 192. 193. 194. 195.
 196. 197. 198. 199. 200. 201. 202. 203. 204. 205. 206. 207. 208. 209.
 210. 211. 212. 213. 214. 215. 216. 217. 218. 219. 220. 221. 222. 223.
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 252. 253. 254. 255. 256. 257. 258. 259. 260. 261. 262. 263. 264. 265.
 266. 267. 268. 269. 270. 271. 272. 273. 274. 275. 276. 277. 278. 279.
 280. 281. 282. 283. 284. 285. 286. 287. 288. 289. 290. 291. 292. 293.
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 308. 309. 310. 311. 312. 313. 314. 315. 316. 317. 318. 319. 320. 321.
 322. 323. 324. 325. 326. 327. 328. 329. 330. 331. 332. 333. 334. 335.
 336. 337. 338. 339. 340. 341. 342. 343. 344. 345. 346. 347. 348. 349.
 350. 351. 352. 353. 354. 355. 356. 357. 358. 359. 360. 361. 362. 363.
 364. 365. 366. 367. 368. 369. 370. 371. 373. 374. 375. 376. 377. 378.
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 393. 394. 395. 396. 397. 398. 399. 400. 401. 402. 403. 404. 405. 406.
 407. 408. 409. 410. 411. 412. 413. 414. 415. 416. 417. 418. 419. 420.
 421. 422. 423. 424. 425. 426. 427. 428. 429. 430. 431. 432. 433. 434.
 435. 436. 437. 438. 439. 440. 441. 442. 443. 444. 445. 446. 447. 448.
 449. 450. 451. 452. 453. 454. 455. 456. 457. 458. 459. 460. 461. 462.
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 477. 478. 479. 480. 481. 482. 483. 484. 485. 486. 487. 488. 489. 490.
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 505. 506. 507. 508. 509. 510. 511. 512. 513. 514. 515. 516. 517. 518.
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 533. 534. 535. 536. 537. 538. 539. 540. 541. 542. 543. 544. 545. 546.
 547. 548. 549. 550. 551. 552. 553. 554. 555. 556. 557. 558. 559. 560.
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 575. 576. 577. 578. 579. 580. 581. 582. 583. 584. 585. 586. 587. 588.
 589. 590. 591. 592. 593. 594. 595. 596. 597. 598. 599. 600. 601. 602.
 603. 604. 605. 606. 607. 608. 609. 610. 611. 612. 613. 614. 615. 616.
 617. 618. 619. 620. 621. 622. 623. 624. 625. 626. 627. 628. 629. 630.
 631. 632. 633. 634. 635. 636. 637. 638. 639. 640. 641. 642. 643. 644.
 645. 646. 647. 648. 649. 650. 651. 652. 653. 654. 655. 656. 657. 658.
 659. 660. 661. 662. 663. 664. 665. 666. 667. 668. 669. 670. 671. 672.
 673. 674. 675. 676. 677. 678. 679. 680. 681. 682. 683. 684. 685. 686.
 687. 688. 689. 690. 691. 692. 693. 694. 695. 696. 697. 698. 699. 700.
 701. 702. 703. 704. 705. 706. 707. 708. 709. 710. 711. 712. 713. 714.
 715. 716. 717. 718. 719. 720. 721. 722. 723. 724. 725. 726. 727. 728.
 729. 730. 731. 732. 733. 734. 735. 736. 737. 738. 739. 740. 741. 742.
 743. 744. 745. 746. 747. 748. 749. 750. 751. 752. 753. 754. 755. 756.
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 785. 786. 787. 788. 789. 790. 791. 792. 793. 794. 795. 796. 797. 798.
 799. 800. 801. 802. 803. 804. 805. 806. 807. 808. 809. 810. 811. 812.
 813. 814. 815. 816. 817. 818. 819. 820. 821. 822. 823. 824. 825. 826.
 827. 828. 829. 830. 831. 832. 833. 834. 835. 836. 837. 838. 839. 840.
 841. 842. 843. 844. 845. 846. 847. 848. 849. 850. 851. 852. 853. 854.
 855. 856. 857. 858. 859. 860. 861. 862. 863. 864. 865. 866. 867. 868.
 869. 870. 871. 872. 873. 874. 875. 876. 877. 878. 879. 880. 881. 882.
 883. 884. 885. 886. 887. 888. 889. 890. 891. 892. 893. 894. 895. 896.
 897. 898. 899. 900. 901. 902. 903. 904. 905. 906. 907. 908. 909. 910.
 911. 912. 913. 914. 915. 916. 917. 918. 919. 920. 921. 922. 923. 924.
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 4.0000e+03 4.0520e+03 4.1500e+03 4.1580e+03 4.2500e+03 4.2640e+03
 4.3700e+03 4.4000e+03 4.4760e+03 4.5000e+03 4.5820e+03 4.6920e+03
 4.7500e+03 4.7980e+03 4.9040e+03 5.0000e+03 5.0100e+03 5.1160e+03
 5.2000e+03 5.2220e+03 5.2770e+03 5.3830e+03 5.4890e+03 5.5540e+03
 5.6190e+03 5.6840e+03 5.7000e+03 5.8490e+03 6.0140e+03 6.1200e+03
 6.2260e+03 6.3320e+03 6.4380e+03 6.5440e+03 6.6500e+03 6.7500e+03
 6.7560e+03 6.8620e+03 6.9680e+03 7.0740e+03 7.1800e+03 7.2860e+03
 7.3920e+03 7.4980e+03 7.5000e+03 7.6040e+03 7.7090e+03 7.8150e+03
 7.9210e+03 8.0000e+03 8.0270e+03 8.1330e+03 8.2390e+03 8.3450e+03
 8.4510e+03 8.5000e+03 8.5570e+03 8.6630e+03 8.7690e+03 8.8750e+03
 8.9850e+03 9.0000e+03 9.0950e+03 9.2050e+03 9.3150e+03 9.4250e+03
 9.5000e+03 9.5350e+03 9.6410e+03 9.7470e+03 9.8530e+03 9.9500e+03
 9.9590e+03 1.0000e+04 1.0065e+04 1.1250e+04 2.1600e+04 5.2030e+04
 1.0406e+05        nan]

V339:  float32, 236, %89.98
[0.0000e+00 5.0000e+00 7.0000e+00 8.0000e+00 1.0000e+01 1.1000e+01
 1.5000e+01 1.7000e+01 1.8000e+01 2.0000e+01 2.4000e+01 2.5000e+01
 2.6000e+01 2.9000e+01 3.0000e+01 3.4000e+01 3.5000e+01 3.7000e+01
 4.0000e+01 4.2000e+01 4.5000e+01 4.7000e+01 5.0000e+01 5.1000e+01
 5.2000e+01 5.5000e+01 6.0000e+01 6.5000e+01 6.7000e+01 6.9000e+01
 7.0000e+01 7.5000e+01 7.6000e+01 8.0000e+01 8.2000e+01 8.3000e+01
 8.5000e+01 8.6000e+01 8.7000e+01 8.8000e+01 9.0000e+01 9.5000e+01
 9.7000e+01 9.8000e+01 1.0000e+02 1.0300e+02 1.0500e+02 1.0600e+02
 1.0700e+02 1.1000e+02 1.1300e+02 1.1500e+02 1.2000e+02 1.2500e+02
 1.2700e+02 1.3000e+02 1.3500e+02 1.4000e+02 1.5000e+02 1.6000e+02
 1.6100e+02 1.6500e+02 1.7000e+02 1.7500e+02 1.8000e+02 1.8500e+02
 1.9000e+02 2.0000e+02 2.0500e+02 2.1000e+02 2.1500e+02 2.2000e+02
 2.2500e+02 2.3500e+02 2.4500e+02 2.5000e+02 2.6000e+02 2.6900e+02
 2.7000e+02 2.7100e+02 2.7400e+02 2.7500e+02 2.7700e+02 2.8000e+02
 3.0000e+02 3.0500e+02 3.2000e+02 3.5000e+02 3.7500e+02 4.0000e+02
 4.3000e+02 4.5000e+02 4.8000e+02 5.0000e+02 5.3000e+02 5.5000e+02
 5.5500e+02 6.0000e+02 6.4000e+02 6.4100e+02 6.8000e+02 7.0000e+02
 7.5000e+02 8.0000e+02 8.0500e+02 8.2500e+02 8.7000e+02 9.0000e+02
 9.7600e+02 1.0000e+03 1.0310e+03 1.0678e+03 1.1000e+03 1.1370e+03
 1.1500e+03 1.1900e+03 1.2000e+03 1.2470e+03 1.2500e+03 1.3000e+03
 1.3020e+03 1.3500e+03 1.3750e+03 1.4120e+03 1.5000e+03 1.5180e+03
 1.6000e+03 1.6240e+03 1.7300e+03 1.8000e+03 1.8360e+03 1.9420e+03
 2.0000e+03 2.0480e+03 2.1540e+03 2.2500e+03 2.2600e+03 2.3560e+03
 2.3660e+03 2.4000e+03 2.4720e+03 2.5000e+03 2.5820e+03 2.6920e+03
 2.8020e+03 2.9080e+03 2.9110e+03 2.9630e+03 2.9910e+03 3.0000e+03
 3.0170e+03 3.0690e+03 3.0970e+03 3.1230e+03 3.1750e+03 3.1910e+03
 3.2030e+03 3.2290e+03 3.2500e+03 3.2810e+03 3.3010e+03 3.3090e+03
 3.3350e+03 3.3560e+03 3.3910e+03 3.4110e+03 3.4150e+03 3.4410e+03
 3.4620e+03 3.5010e+03 3.5210e+03 3.5470e+03 3.5680e+03 3.6000e+03
 3.6110e+03 3.6310e+03 3.6530e+03 3.6570e+03 3.6660e+03 3.6740e+03
 3.7120e+03 3.7410e+03 3.7590e+03 3.7720e+03 3.7800e+03 3.8650e+03
 3.8780e+03 3.8860e+03 3.9710e+03 3.9840e+03 3.9920e+03 4.0420e+03
 4.0770e+03 4.0900e+03 4.0980e+03 4.1960e+03 4.2040e+03 4.2500e+03
 4.2540e+03 4.3060e+03 4.3100e+03 4.3600e+03 4.4000e+03 4.4120e+03
 4.4150e+03 4.4280e+03 4.5000e+03 4.5180e+03 4.6240e+03 4.7300e+03
 4.7580e+03 4.8360e+03 4.8640e+03 4.8910e+03 4.9700e+03 4.9970e+03
 5.0000e+03 5.0760e+03 5.1030e+03 5.1680e+03 5.1820e+03 5.2330e+03
 5.2880e+03 5.2980e+03 5.4630e+03 5.5730e+03 5.6280e+03 6.7500e+03
 7.5000e+03 8.5000e+03 9.0000e+03 1.0000e+04 1.1250e+04 2.1600e+04
 5.2030e+04 1.0406e+05        nan]

# removing high correlated variables (222 eliminated)
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
len(drop_col)

222

# dropping redundant Vs
train = train.drop(drop_col, axis=1)
test = test.drop(drop_col, axis=1)

# remaining Vs lenght (64 vars)
columns=[col for col in train.columns if re.search('^V\d*', col)]
len(columns)

64

plt.figure(figsize=(10,10))
sns.heatmap(train[columns+['isFraud']].sample(frac=0.2).corr(),annot=False, cmap="RdBu_r")

<Axes: >

#pickling datasets
#Save 'train' data to a pickle file named 'train_2.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_2.pkl')

#save 'test' data to a pickle file named 'test_2.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_2.pkl')

# Read the 'train_2.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_2.pkl')

# Read the 'test_2.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_2.pkl')

id_1 ... id_11 (numeric)

column_details(regex='id_(1|2|3|4|5|6|7|8|9|10|11)$', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

id_10:  float64, 59, %86.74
[-100.  -68.  -64.  -60.  -59.  -58.  -57.  -56.  -55.  -54.  -51.  -50.
  -49.  -47.  -44.  -43.  -42.  -41.  -40.  -39.  -38.  -37.  -36.  -35.
  -34.  -33.  -32.  -31.  -30.  -29.  -28.  -27.  -26.  -25.  -24.  -23.
  -22.  -21.  -20.  -19.  -18.  -17.  -16.  -15.  -14.  -13.  -12.  -11.
  -10.   -9.   -8.   -7.   -6.   -5.   -4.   -3.   -2.   -1.    0.   nan]

id_11:  float64, 346, %74.59
[ 90.          90.09999847  90.11000061  90.16000366  90.19999695
  90.20999908  90.23999786  90.26999664  90.27999878  90.29000092
  90.31999969  90.34999847  90.36000061  90.37999725  90.41000366
  90.43000031  90.48000336  90.51999664  90.52999878  90.54000092
  90.55000305  90.56999969  90.58999634  90.62999725  90.65000153
  90.69999695  90.73999786  90.76999664  90.80000305  90.83000183
  90.84999847  90.91000366  91.          91.02999878  91.06999969
  91.08999634  91.11000061  91.13999939  91.15000153  91.18000031
  91.20999908  91.25        91.27999878  91.30000305  91.33999634
  91.36000061  91.40000153  91.43000031  91.44999695  91.45999908
  91.48999786  91.51000214  91.52999878  91.56999969  91.58000183
  91.58999634  91.59999847  91.66999817  91.73999786  91.76000214
  91.77999878  91.80000305  91.81999969  91.87999725  91.88999939
  91.91999817  91.94000244  91.94999695  92.          92.05999756
  92.08000183  92.11000061  92.12999725  92.19000244  92.20999908
  92.23000336  92.23999786  92.30999756  92.37000275  92.41000366
  92.44999695  92.5         92.54000092  92.55000305  92.55999756
  92.58999634  92.62000275  92.62999725  92.68000031  92.69999695
  92.70999908  92.73000336  92.73999786  92.75        92.76999664
  92.77999878  92.86000061  92.88999939  92.94000244  93.
  93.01999664  93.05999756  93.09999847  93.13999939  93.15000153
  93.18000031  93.22000122  93.23999786  93.26000214  93.33000183
  93.37000275  93.38999939  93.40000153  93.41999817  93.44000244
  93.48000336  93.55000305  93.58000183  93.58999634  93.62000275
  93.65000153  93.66999817  93.68000031  93.75        93.80999756
  93.83000183  93.84999847  93.88999939  93.94000244  93.95999908
  93.97000122  93.98000336  94.          94.02999878  94.05000305
  94.05999756  94.12000275  94.16000366  94.16999817  94.19000244
  94.19999695  94.23000336  94.25        94.29000092  94.31999969
  94.37000275  94.37999725  94.40000153  94.44000244  94.5
  94.51999664  94.52999878  94.55000305  94.56999969  94.58999634
  94.62000275  94.63999939  94.66999817  94.68000031  94.73999786
  94.76000214  94.79000092  94.80999756  94.83000183  94.87000275
  94.91999817  94.93000031  94.94000244  94.98000336  95.
  95.04000092  95.05000305  95.05999756  95.06999969  95.08000183
  95.11000061  95.15000153  95.16000366  95.18000031  95.19000244
  95.20999908  95.23999786  95.26000214  95.27999878  95.29000092
  95.30999756  95.34999847  95.36000061  95.37999725  95.40000153
  95.41000366  95.41999817  95.44999695  95.51000214  95.55999756
  95.58999634  95.59999847  95.65000153  95.69000244  95.69999695
  95.70999908  95.73999786  95.76999664  95.80000305  95.80999756
  95.83000183  95.86000061  95.88999939  95.94000244  95.95999908
  96.          96.02999878  96.04000092  96.05000305  96.08000183
  96.09999847  96.12000275  96.15000153  96.19000244  96.19999695
  96.23000336  96.25        96.26000214  96.30000305  96.33999634
  96.36000061  96.38999939  96.40000153  96.43000031  96.47000122
  96.48999786  96.51000214  96.55000305  96.58999634  96.62999725
  96.63999939  96.66999817  96.69000244  96.69999695  96.72000122
  96.73999786  96.76999664  96.83000183  96.83999634  96.87999725
  96.91999817  96.94000244  96.97000122  97.          97.01000214
  97.02999878  97.05999756  97.08999634  97.09999847  97.11000061
  97.12000275  97.13999939  97.16000366  97.16999817  97.19999695
  97.22000122  97.23999786  97.25        97.26000214  97.26999664
  97.30000305  97.33000183  97.34999847  97.37000275  97.38999939
  97.40000153  97.41000366  97.44000244  97.44999695  97.47000122
  97.5         97.51999664  97.52999878  97.54000092  97.55000305
  97.55999756  97.58999634  97.62000275  97.65000153  97.66000366
  97.66999817  97.69999695  97.73000336  97.75        97.77999878
  97.80000305  97.83000183  97.84999847  97.87000275  97.88999939
  97.94000244  97.95999908  97.97000122  97.98000336  97.98999786
  98.          98.01999664  98.02999878  98.05999756  98.08000183
  98.09999847  98.11000061  98.12999725  98.15000153  98.18000031
  98.19999695  98.25        98.29000092  98.30999756  98.31999969
  98.33000183  98.33999634  98.34999847  98.36000061  98.38999939
  98.40000153  98.41000366  98.44000244  98.48000336  98.51000214
  98.51999664  98.54000092  98.58999634  98.69000244  98.69999695
  98.73000336  98.76999664  98.81999969  98.90000153  98.91000366
  98.91999817  98.93000031  98.94000244  98.94999695  98.98000336
  99.01000214  99.04000092  99.09999847  99.13999939  99.22000122
 100.                  nan]

# removing high correlated variables 
corr_treshold = 0.75
drop_col = remove_collinear_features(train[columns],corr_treshold)
len(drop_col)

0

plt.figure(figsize=(10,10))
sns.heatmap(train[columns].sample(frac=0.2).corr(),annot=False, cmap="RdBu_r")

<Axes: >

There is no correlation between these two variables, we are remaining them.

id_12...id_38 (nominal categoric)

column_details(regex='id_(12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38)', df=train)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

id_12:  object, 2, %74.02
['Found' 'NotFound' nan]

id_13:  object, 52, %77.38
['10.0' '11.0' '12.0' '13.0' '14.0' '15.0' '17.0' '18.0' '19.0' '20.0'
 '21.0' '22.0' '23.0' '24.0' '25.0' '26.0' '28.0' '29.0' '30.0' '31.0'
 '32.0' '33.0' '34.0' '35.0' '36.0' '37.0' '38.0' '39.0' '40.0' '41.0'
 '43.0' '44.0' '45.0' '46.0' '47.0' '48.0' '49.0' '50.0' '51.0' '52.0'
 '53.0' '54.0' '55.0' '56.0' '57.0' '58.0' '59.0' '60.0' '61.0' '62.0'
 '63.0' '64.0' nan]

id_14:  object, 25, %85.14
['-120.0' '-180.0' '-210.0' '-240.0' '-300.0' '-360.0' '-420.0' '-480.0'
 '-540.0' '-600.0' '-660.0' '0.0' '120.0' '180.0' '240.0' '270.0' '300.0'
 '330.0' '360.0' '420.0' '480.0' '540.0' '60.0' '600.0' '720.0' nan]

id_15:  object, 3, %74.59
['Found' 'New' 'Unknown' nan]

id_16:  object, 2, %76.65
['Found' 'NotFound' nan]

id_17:  object, 101, %74.83
['100.0' '101.0' '102.0' '105.0' '106.0' '107.0' '110.0' '111.0' '112.0'
 '114.0' '116.0' '117.0' '118.0' '119.0' '120.0' '121.0' '122.0' '123.0'
 '124.0' '126.0' '127.0' '128.0' '129.0' '130.0' '131.0' '133.0' '134.0'
 '135.0' '136.0' '137.0' '138.0' '139.0' '140.0' '141.0' '142.0' '143.0'
 '144.0' '145.0' '146.0' '147.0' '148.0' '149.0' '150.0' '151.0' '152.0'
 '153.0' '154.0' '155.0' '156.0' '157.0' '158.0' '159.0' '160.0' '161.0'
 '162.0' '163.0' '164.0' '166.0' '168.0' '171.0' '173.0' '175.0' '177.0'
 '178.0' '180.0' '182.0' '183.0' '184.0' '185.0' '186.0' '188.0' '189.0'
 '190.0' '191.0' '192.0' '194.0' '195.0' '197.0' '198.0' '199.0' '200.0'
 '201.0' '202.0' '203.0' '205.0' '207.0' '208.0' '210.0' '211.0' '212.0'
 '213.0' '214.0' '216.0' '217.0' '218.0' '219.0' '220.0' '225.0' '226.0'
 '228.0' '229.0' nan]

id_19:  object, 505, %74.83
['100.0' '101.0' '102.0' '103.0' '104.0' '105.0' '106.0' '107.0' '108.0'
 '109.0' '110.0' '112.0' '113.0' '114.0' '116.0' '117.0' '118.0' '119.0'
 '120.0' '121.0' '122.0' '123.0' '124.0' '125.0' '127.0' '128.0' '129.0'
 '130.0' '131.0' '132.0' '133.0' '134.0' '136.0' '137.0' '138.0' '139.0'
 '140.0' '141.0' '142.0' '144.0' '145.0' '146.0' '147.0' '148.0' '149.0'
 '150.0' '151.0' '152.0' '153.0' '154.0' '155.0' '156.0' '157.0' '158.0'
 '160.0' '161.0' '162.0' '163.0' '164.0' '165.0' '166.0' '167.0' '170.0'
 '171.0' '173.0' '175.0' '176.0' '177.0' '178.0' '179.0' '180.0' '182.0'
 '184.0' '185.0' '186.0' '187.0' '188.0' '189.0' '190.0' '192.0' '193.0'
 '194.0' '195.0' '196.0' '197.0' '201.0' '202.0' '203.0' '204.0' '205.0'
 '207.0' '208.0' '209.0' '210.0' '211.0' '212.0' '214.0' '215.0' '216.0'
 '218.0' '219.0' '220.0' '221.0' '222.0' '223.0' '224.0' '225.0' '226.0'
 '228.0' '229.0' '231.0' '232.0' '233.0' '234.0' '235.0' '236.0' '237.0'
 '238.0' '239.0' '240.0' '241.0' '242.0' '243.0' '244.0' '245.0' '246.0'
 '247.0' '248.0' '249.0' '250.0' '251.0' '252.0' '253.0' '254.0' '256.0'
 '257.0' '258.0' '259.0' '260.0' '261.0' '262.0' '263.0' '265.0' '266.0'
 '267.0' '268.0' '269.0' '270.0' '271.0' '272.0' '273.0' '274.0' '275.0'
 '276.0' '277.0' '278.0' '279.0' '280.0' '281.0' '282.0' '283.0' '284.0'
 '287.0' '288.0' '289.0' '290.0' '291.0' '292.0' '294.0' '295.0' '296.0'
 '297.0' '298.0' '299.0' '300.0' '301.0' '302.0' '303.0' '304.0' '305.0'
 '306.0' '307.0' '308.0' '309.0' '310.0' '311.0' '312.0' '313.0' '314.0'
 '315.0' '316.0' '317.0' '318.0' '319.0' '320.0' '321.0' '322.0' '324.0'
 '325.0' '326.0' '327.0' '328.0' '330.0' '332.0' '333.0' '335.0' '337.0'
 '339.0' '340.0' '341.0' '342.0' '343.0' '344.0' '345.0' '346.0' '347.0'
 '348.0' '350.0' '351.0' '352.0' '353.0' '355.0' '356.0' '358.0' '359.0'
 '360.0' '361.0' '362.0' '363.0' '364.0' '365.0' '366.0' '367.0' '368.0'
 '369.0' '370.0' '371.0' '372.0' '373.0' '374.0' '375.0' '376.0' '377.0'
 '378.0' '379.0' '380.0' '381.0' '382.0' '383.0' '384.0' '385.0' '387.0'
 '388.0' '389.0' '390.0' '391.0' '393.0' '394.0' '395.0' '396.0' '397.0'
 '398.0' '399.0' '400.0' '401.0' '402.0' '403.0' '404.0' '405.0' '406.0'
 '407.0' '409.0' '410.0' '411.0' '412.0' '413.0' '414.0' '415.0' '416.0'
 '417.0' '418.0' '419.0' '420.0' '421.0' '422.0' '424.0' '425.0' '426.0'
 '427.0' '428.0' '429.0' '430.0' '431.0' '432.0' '433.0' '434.0' '435.0'
 '436.0' '437.0' '438.0' '439.0' '440.0' '441.0' '442.0' '443.0' '444.0'
 '445.0' '446.0' '447.0' '449.0' '451.0' '452.0' '453.0' '454.0' '455.0'
 '456.0' '457.0' '458.0' '459.0' '460.0' '462.0' '463.0' '466.0' '467.0'
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id_29:  object, 2, %74.59
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id_30:  object, 72, %85.6
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id_31:  object, 110, %74.66
['BLU/Dash' 'Cherry' 'Generic/Android' 'Generic/Android 7.0' 'Inco/Minion'
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 'chrome 43.0 for android' 'chrome 46.0 for android' 'chrome 49.0'
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id_35:  object, 2, %74.59
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id_36:  object, 2, %74.59
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id_37:  object, 2, %74.59
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id_38:  object, 2, %74.59
['F' 'T' nan]

# Syncronize train and test
for col in [ 'id_12', 'id_13', 'id_14','id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']: 
  old_versions_col= set(train[col].unique()) - set(test[col].unique())
  new_versions_col = set(test[col].unique()) - set(train[col].unique()) 
  test[col] =test[col].apply(lambda x: np.nan if x in new_versions_col else x)
  train[col] =train[col].apply(lambda x: np.nan if x in old_versions_col else x)

rareIds = []

# Specify the range of columns you want to process
columns_to_process = ['id_12', 'id_13', 'id_14',
           'id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']

for col in columns_to_process:
    for k, df in enumerate([train, test]):
        rareIds = df[col].value_counts()
        rareIds = [value for value in rareIds.index if rareIds[value] < 3]
        rareIds += rareIds

        print(f"{('TEST' if k else 'TRAIN')}")
        print(f"Number of unique in {col}: {df[col].nunique()}")
        print(f"Number of unique values with frequency less than 3 in {col}: {len(rareIds)}\n")

rareIds = set(rareIds)

TRAIN
Number of unique in id_12: 2
Number of unique values with frequency less than 3 in id_12: 0

TEST
Number of unique in id_12: 2
Number of unique values with frequency less than 3 in id_12: 0

TRAIN
Number of unique in id_13: 19
Number of unique values with frequency less than 3 in id_13: 2

TEST
Number of unique in id_13: 19
Number of unique values with frequency less than 3 in id_13: 2

TRAIN
Number of unique in id_14: 18
Number of unique values with frequency less than 3 in id_14: 0

TEST
Number of unique in id_14: 18
Number of unique values with frequency less than 3 in id_14: 4

TRAIN
Number of unique in id_15: 3
Number of unique values with frequency less than 3 in id_15: 0

TEST
Number of unique in id_15: 3
Number of unique values with frequency less than 3 in id_15: 0

TRAIN
Number of unique in id_17: 59
Number of unique values with frequency less than 3 in id_17: 12

TEST
Number of unique in id_17: 59
Number of unique values with frequency less than 3 in id_17: 34

TRAIN
Number of unique in id_19: 366
Number of unique values with frequency less than 3 in id_19: 36

TEST
Number of unique in id_19: 366
Number of unique values with frequency less than 3 in id_19: 242

TRAIN
Number of unique in id_30: 68
Number of unique values with frequency less than 3 in id_30: 2

TEST
Number of unique in id_30: 68
Number of unique values with frequency less than 3 in id_30: 2

TRAIN
Number of unique in id_31: 85
Number of unique values with frequency less than 3 in id_31: 4

TEST
Number of unique in id_31: 85
Number of unique values with frequency less than 3 in id_31: 6

TRAIN
Number of unique in id_32: 4
Number of unique values with frequency less than 3 in id_32: 0

TEST
Number of unique in id_32: 4
Number of unique values with frequency less than 3 in id_32: 2

TRAIN
Number of unique in id_34: 3
Number of unique values with frequency less than 3 in id_34: 0

TEST
Number of unique in id_34: 3
Number of unique values with frequency less than 3 in id_34: 0

TRAIN
Number of unique in id_36: 2
Number of unique values with frequency less than 3 in id_36: 0

TEST
Number of unique in id_36: 2
Number of unique values with frequency less than 3 in id_36: 0

TRAIN
Number of unique in id_37: 2
Number of unique values with frequency less than 3 in id_37: 0

TEST
Number of unique in id_37: 2
Number of unique values with frequency less than 3 in id_37: 0

TRAIN
Number of unique in id_38: 2
Number of unique values with frequency less than 3 in id_38: 0

TEST
Number of unique in id_38: 2
Number of unique values with frequency less than 3 in id_38: 0

# low frequency cats will change into nans
columns_to_process = [i for i in ['id_12', 'id_13', 'id_14',
           'id_15', 'id_17', 'id_19', 'id_30', 'id_31', 'id_32', 'id_34', 'id_36', 'id_37', 'id_38']]  

for col in columns_to_process:
    for df in [train, test]:
        df[col] = df[col].apply(lambda x: np.nan if x in rareIds else x)

id_columns = train.loc[:, 'id_12':'id_38']

# Create an empty DataFrame to store the results
cramers_v_matrix = pd.DataFrame(index=id_columns.columns, columns=id_columns.columns, dtype=float)

# Fill in the Cramers V values for each pair of columns
for col1 in id_columns.columns:
    for col2 in id_columns.columns:
        cramers_v_matrix.loc[col1, col2] = cramers_v(id_columns[col1], id_columns[col2])

# Heatmap
plt.figure(figsize=(12, 10))
sns.heatmap(cramers_v_matrix, cmap='RdBu_r', annot=True, fmt=".2f", linewidths=.5)
plt.title('Cramers V Matrix Heatmap')
plt.show()

# We want to remove collinear features in 'id_' columns with a threshold of 0.75
id_columns = [col for col in train.columns if re.search('^id_(12|13|14|15|16|17|18|19|20|21|22|23|24|25|26|27|28|29|30|31|32|33|34|35|36|37|38)', col)]
drop_columns = identify_collinear_categorical_features(train, id_columns, threshold=0.75)

# Display the columns to drop
print("Columns to drop:", drop_columns)

Columns to drop: ['id_29', 'id_20', 'id_28', 'id_16', 'id_37', 'id_33', 'id_35']

# Remove the collinear features
train = remove_collinear_categorical_features(train, drop_columns)
test = remove_collinear_categorical_features(test, drop_columns)

# Select only the columns that start with 'id_' and end with a number between 12 and 38
remaining_features = [col for col in train.columns if col.startswith('id_') and col[3:].isdigit() and 12 <= int(col[3:]) <= 38 and col not in ['id_35', 'id_29', 'id_20', 'id_33', 'id_28', 'id_16']]

for col in remaining_features:
    distinct_count = train[col].nunique()
    
    if distinct_count < 200:
        # Target encode using target mean
        temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
        train[col + '_target_encoded'] = train[col].replace(temp_dict)
        test[col + '_target_encoded'] = test[col].replace(temp_dict)
    else:
        # Frequency encode
        train, test = frequency_encoding(train, test, columns=[col])

# Display the first few rows of the modified data
print(train.head())
print(test.head())

               isFraud  TransactionDT  TransactionAmt  dist1   C5   C13  \
TransactionID                                                             
2987000              0          86400            68.5   19.0  0.0   1.0   
2987001              0          86401            29.0    NaN  0.0   1.0   
2987002              0          86469            59.0  287.0  0.0   1.0   
2987003              0          86499            50.0    NaN  0.0  25.0   
2987004              0          86506            50.0    NaN  0.0   1.0   

                  D1     D2    D3    D4  ...  id_14_target_encoded  \
TransactionID                            ...                         
2987000         14.0    NaN  13.0   NaN  ...                   NaN   
2987001          0.0    NaN   NaN   0.0  ...                   NaN   
2987002          0.0    NaN   NaN   0.0  ...                   NaN   
2987003        112.0  112.0   0.0  94.0  ...                   NaN   
2987004          0.0    NaN   NaN   NaN  ...              0.032246   

               id_15_target_encoded  id_17_target_encoded  id_19_freq_encoded  \
TransactionID                                                                   
2987000                         NaN                   NaN            0.766912   
2987001                         NaN                   NaN            0.766912   
2987002                         NaN                   NaN            0.766912   
2987003                         NaN                   NaN            0.766912   
2987004                    0.047129               0.04181            0.008663   

               id_30_target_encoded  id_31_target_encoded  \
TransactionID                                               
2987000                         NaN                   NaN   
2987001                         NaN                   NaN   
2987002                         NaN                   NaN   
2987003                         NaN                   NaN   
2987004                    0.053551               0.07109   

               id_32_target_encoded  id_34_target_encoded  \
TransactionID                                               
2987000                         NaN                   NaN   
2987001                         NaN                   NaN   
2987002                         NaN                   NaN   
2987003                         NaN                   NaN   
2987004                     0.06345              0.037746   

               id_36_target_encoded  id_38_target_encoded  
TransactionID                                              
2987000                         NaN                   NaN  
2987001                         NaN                   NaN  
2987002                         NaN                   NaN  
2987003                         NaN                   NaN  
2987004                    0.078535              0.060191  

[5 rows x 142 columns]
               isFraud  TransactionDT  TransactionAmt  dist1    C5    C13  \
TransactionID                                                               
3429905              0       11246665           30.95    NaN   1.0    1.0   
3429906              0       11246704           53.95    0.0   1.0    3.0   
3429907              0       11246761          117.00    2.0  95.0  409.0   
3429908              0       11246761           29.00    5.0   0.0   89.0   
3429909              0       11247072          200.00    NaN   0.0    2.0   

                  D1     D2     D3     D4  ...  id_14_target_encoded  \
TransactionID                              ...                         
3429905          0.0    NaN    NaN    NaN  ...                   NaN   
3429906        549.0  549.0  362.0    NaN  ...                   NaN   
3429907        178.0  178.0   15.0  559.0  ...                   NaN   
3429908        163.0  163.0    0.0  163.0  ...                   NaN   
3429909          0.0    NaN    NaN   57.0  ...              0.032246   

               id_15_target_encoded  id_17_target_encoded  id_19_freq_encoded  \
TransactionID                                                                   
3429905                         NaN                   NaN            0.766912   
3429906                         NaN                   NaN            0.766912   
3429907                         NaN                   NaN            0.766912   
3429908                         NaN                   NaN            0.766912   
3429909                    0.047129               0.04181            0.008663   

               id_30_target_encoded  id_31_target_encoded  \
TransactionID                                               
3429905                         NaN                   NaN   
3429906                         NaN                   NaN   
3429907                         NaN                   NaN   
3429908                         NaN                   NaN   
3429909                    0.078125              0.101573   

               id_32_target_encoded  id_34_target_encoded  \
TransactionID                                               
3429905                         NaN                   NaN   
3429906                         NaN                   NaN   
3429907                         NaN                   NaN   
3429908                         NaN                   NaN   
3429909                     0.06345               0.06236   

               id_36_target_encoded  id_38_target_encoded  
TransactionID                                              
3429905                         NaN                   NaN  
3429906                         NaN                   NaN  
3429907                         NaN                   NaN  
3429908                         NaN                   NaN  
3429909                    0.078535              0.093292  

[5 rows x 142 columns]

# Drop the original columns
train.drop(remaining_features, axis=1, inplace=True)
test.drop(remaining_features, axis=1, inplace=True)

pd.DataFrame(train.dtypes).to_clipboard()

DeviceType (nominal categoric)

for df in [train, test]:
  column_details(regex='DeviceType', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

DeviceType:  object, 2, %74.62
['desktop' 'mobile' nan]

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

DeviceType:  object, 2, %80.75
['desktop' 'mobile' nan]

plot_col('DeviceType', df=train)

fraud_rates_device_type = train.groupby('DeviceType')['isFraud'].mean().reset_index()
fraud_rates_device_type.rename(columns={'isFraud': 'FraudRate'}, inplace=True)
fraud_rates_device_type.sort_values(by='FraudRate', ascending=False, inplace=True)

print(fraud_rates_device_type)

  DeviceType  FraudRate
1     mobile   0.098887
0    desktop   0.061458

#target encoding
col = 'DeviceType'
temp_dict = train.groupby([col])['isFraud'].agg(['mean']).to_dict()['mean']
train[col+'_target_encoded'] = train[col].replace(temp_dict)
test[col+'_target_encoded'] = test[col].replace(temp_dict)

# Drop the original column
train.drop('DeviceType', axis=1, inplace=True)
test.drop('DeviceType', axis=1, inplace=True)

DeviceInfo(nominal categoric)

for df in [train, test]:
  column_details(regex='DeviceInfo', df=df)

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

DeviceInfo:  object, 1583, %78.49
['0PAJ5' '0PJA2' '0PM92' ... 'verykools5035' 'xs-Z47b7VqTMxs' nan]

Unique Values of the Features:
feature: DTYPE, NUNIQUE, NULL_RATE

DeviceInfo:  object, 962, %84.16
['2PYB2' '4009F' '4013M Build/KOT49H' '4047A Build/NRD90M'
 '4047G Build/NRD90M' '47418' '5010G Build/MRA58K' '5010S Build/MRA58K'
 '5011A Build/NRD90M' '5012G Build/MRA58K' '5015A Build/LMY47I'
 '5025G Build/LMY47I' '5044A' '5049W Build/NRD90M' '5051A Build/MMB29M'
 '5054S Build/LMY47V' '5056A Build/MMB29M' '5056N' '5080A Build/MRA58K'
 '5085B Build/MRA58K' '6037B' '6039A Build/LRX22G' '6045I Build/LRX22G'
 '6055B' '7048A Build/LRX22G' '7_Plus' '8050G Build/LMY47I'
 '8080 Build/LRX21M' '9008A Build/NRD90M' 'A0001' 'A3-A20' 'A463BG'
 'A574BL Build/NMF26F' 'A577VL' 'AERIAL' 'AKUS' 'ALCATEL'
 'ALCATEL ONE TOUCH 7047A Build/JDQ39' 'ALE-L21 Build/HuaweiALE-L21'
 'ALE-L23 Build/HuaweiALE-L23' 'ALP-L09 Build/HUAWEIALP-L09'
 'ALP-L09 Build/HUAWEIALP-L09S' 'AM508' 'ANE-LX3 Build/HUAWEIANE-LX3'
 'ASUS_A001' 'ASUS_X008D Build/NRD90M' 'ASUS_X00HD Build/NMF26F'
 'ASUS_X00ID' 'ASUS_X018D' 'ASUS_Z00AD Build/LRX21V' 'ASUS_Z017D'
 'ASUS_Z01BDC' 'AX1060' 'AX1070' 'AX820 Build/MRA58K' 'AX821 Build/MRA58K'
 'AX921 Build/MRA58K' 'Alcatel' 'Alcatel_4060O Build/MMB29M'
 'Alcatel_5044R Build/NRD90M' 'Alcatel_5098O Build/MMB29M'
 'Alumini3 Build/MRA58K' 'Alumini3Plus' 'Android 4.4.2' 'Android 5.1'
 'Android 5.1.1' 'Android 6.0' 'Android 6.0.1' 'Android 7.0'
 'Android 7.1.2' 'Aquaris' 'Aquaris V Build/N2G47H'
 'Aquaris X Build/NMF26F' 'Aquaris X5 Plus Build/NMF26F' 'Aquaris_A4.5'
 'Archos' 'Azumi_KINZO_A5_QL' 'B1-750' 'BAC-L03 Build/HUAWEIBAC-L03'
 'BBB100-1' 'BLA-L29 Build/HUAWEIBLA-L29' 'BLADE A520 Build/NRD90M'
 'BLADE A602 Build/MRA58K' 'BLADE L7 Build/MRA58K' 'BLADE V7 Build/MRA58K'
 'BLADE V8 Build/NRD90M' 'BLADE V8 SE Build/NRD90M'
 'BLADE V8Q Build/N2G47H' 'BLL-L23 Build/HUAWEIBLL-L23'
 'BLN-L21 Build/HONORBLN-L21' 'BLU' 'BLU ENERGY X PLUS Build/LRX21M'
 'BLU LIFE XL Build/L050U' 'BND-L34' 'BV8000Pro' 'Blade A465 Build/LMY47D'
 'Blade A510 Build/MRA58K' 'Blade L2 Plus Build/KOT49H'
 'Blade L5 Build/LMY47I' 'Blade V580 Build/LMY47D' 'Blade V6 Build/LRX22G'
 'Blade V6 Max Build/MRA58K' 'Blade V6 Plus Build/MRA58K' 'Bolt'
 'Build/KOT49H' 'Build/OPM1.171019.011' 'Build/OPR1.170623.032'
 'Build/OPR6.170623.013' 'C6603' 'C6903' 'C6906 Build/14.6.A.1.236'
 'CAM-L03 Build/HUAWEICAM-L03' 'CHC-U03 Build/HuaweiCHC-U03' 'CLT-L09'
 'CPH1607' 'CPH1723' 'CRO-L03 Build/HUAWEICRO-L03' 'D2306'
 'D2306 Build/18.6.A.0.182' 'D2406' 'D5306 Build/19.4.A.0.182'
 'D5316 Build/19.4.A.0.182' 'D5503' 'D6503' 'D6603 Build/23.5.A.1.291'
 'DASH' 'DLI-L22 Build/HONORDLI-L22' 'DOMOS' 'Dream'
 'E2104 Build/24.0.A.5.14' 'E2306 Build/26.1.A.3.111'
 'E2306 Build/26.3.A.1.33' 'E501' 'E5306' 'E5306 Build/27.3.A.0.129'
 'E5506' 'E5506 Build/29.1.A.0.101' 'E5506 Build/29.2.A.0.166'
 'E5606 Build/30.2.A.1.21' 'E5823' 'E5823 Build/32.4.A.1.54' 'E6553'
 'E6603 Build/32.4.A.1.54' 'E6683' 'E6790TM' 'E6833'
 'E6853 Build/32.4.A.1.54' 'EGO' 'EML-L29 Build/HUAWEIEML-L29'
 'EVA-L09 Build/HUAWEIEVA-L09' 'EVA-L19 Build/HUAWEIEVA-L19'
 'Energy X 2 Build/E050L' 'F3111 Build/33.3.A.1.115'
 'F3111 Build/33.3.A.1.97' 'F3113' 'F3113 Build/33.3.A.1.97'
 'F3213 Build/36.0.A.2.146' 'F3213 Build/36.1.A.1.86' 'F3311'
 'F3313 Build/37.0.A.2.108' 'F3313 Build/37.0.A.2.248' 'F5121'
 'F5121 Build/34.3.A.0.252' 'F5121 Build/34.4.A.2.19' 'F5122' 'F5321'
 "F80'S+" 'F8331' 'F8332' 'FEVER' 'FIG-LX3 Build/HUAWEIFIG-LX3' 'FP2'
 'FRD-L09 Build/HUAWEIFRD-L09' 'Fusion5_u7' 'G3123'
 'G3123 Build/40.0.A.6.175' 'G3123 Build/40.0.A.6.189' 'G3223'
 'G3223 Build/42.0.A.4.101' 'G3223 Build/42.0.A.4.167'
 'G3313 Build/43.0.A.5.79' 'G3313 Build/43.0.A.7.25' 'G3423' 'G630-U251'
 'G8141' 'G8142' 'GRANT' 'GT-I8190L Build/JZO54K' 'GT-I8190N'
 'GT-I9060L Build/JDQ39' 'GT-I9060M Build/KTU84P' 'GT-I9195L'
 'GT-I9505 Build/LRX22C' 'GT-I9506' 'GT-I9515' 'GT-N5110 Build/JDQ39'
 'GT-N8010' 'GT-P5210 Build/JDQ39' 'GT-P5210 Build/KOT49H'
 'GT-S7580L Build/JDQ39' 'GT-S7582' 'Grand' 'Gravity Build/NRD90M' 'H3321'
 'HTC' 'HTC Desire 10 lifestyle Build/MMB29M'
 'HTC Desire 526G Build/KOT49H' 'HTC Desire 530 Build/MMB29M'
 'HTC Desire 626s Build/LMY47O' 'HTC Desire 650 Build/MMB29M'
 'HTC One A9 Build/NRD90M' 'HTC One A9s Build/MRA58K'
 'HTC One M9 Build/NRD90M' 'HTC One mini Build/KOT49H'
 'HTC One_M8 Build/MRA58K' 'HTC U11 Build/NMF26X' 'HTC6535LVW'
 'HTC6545LVW' 'HTC_Desire_820' 'HTC_One' 'HTC_One_M8/4.28.502.2' 'HUAWEI'
 'HUAWEI Build/MMB28B' 'HUAWEI CAN-L11 Build/HUAWEICAN-L11'
 'HUAWEI CUN-L03 Build/HUAWEICUN-L03' 'HUAWEI G7-L03 Build/HuaweiG7-L03'
 'HUAWEI NXT-L09 Build/HUAWEINXT-L09' 'HUAWEI RIO-L03 Build/HUAWEIRIO-L03'
 'HUAWEI TAG-L13 Build/HUAWEITAG-L13' 'HUAWEI VNS-L21 Build/HUAWEIVNS-L21'
 'HUAWEI VNS-L23 Build/HUAWEIVNS-L23' 'HUAWEI VNS-L31 Build/HUAWEIVNS-L31'
 'HUAWEI VNS-L53 Build/HUAWEIVNS-L53'
 'HUAWEI Y360-U23 Build/HUAWEIY360-U23'
 'HUAWEI Y520-U03 Build/HUAWEIY520-U03'
 'HUAWEI Y560-L03 Build/HUAWEIY560-L03' 'Hisense E51 Build/LMY47V'
 'Hisense F102 Build/NRD90M' 'Hisense F20 Build/MMB29M'
 'Hisense F23 Build/NRD90M' 'Hisense F24 Build/NRD90M'
 'Hisense F32 Build/NMF26F' 'Hisense F8 MINI Build/NRD90M'
 'Hisense L675 Build/MRA58K' 'Hisense L675 PRO Build/NRD90M'
 'Hisense T963 Build/MRA58K' 'Hisense U963 Build/MRA58K' 'ILIUM' 'IO'
 'Ilium' 'Ilium L1000 Build/LRX22G' 'Ilium L1120 Build/NRD90M'
 'Ilium L610 Build/MRA58K' 'Ilium L910 Build/MRA58K'
 'Ilium LT500 Build/LMY47O' 'Ilium LT510 Build/MRA58K'
 'Ilium Pad T7X Build/LMY47I' 'Ilium X210 Build/LMY47I'
 'Ilium X220 Build/MRA58K' 'Ilium X510 Build/MRA58K'
 'Ilium X520 Build/NRD90M' 'Ilium X710 Build/MRA58K' 'KFDOWI Build/LVY48F'
 'KFFOWI Build/LVY48F' 'KFGIWI Build/LVY48F' 'KFSAWI'
 'KFSUWI Build/LVY48F' 'KFTBWI Build/LVY48F' 'Kylin'
 'LDN-LX3 Build/HUAWEILDN-LX3' 'LG-D320 Build/KOT49I.V10a'
 'LG-D331 Build/LRX22G' 'LG-D373' 'LG-D373 Build/KOT49I.V10a'
 'LG-D680 Build/KOT49I' 'LG-D693n Build/LRX22G' 'LG-D722' 'LG-D850'
 'LG-D855' 'LG-D855 Build/LRX21R' 'LG-E975' 'LG-H320 Build/LRX21Y'
 'LG-H420 Build/LRX21Y' 'LG-H500 Build/LRX21Y' 'LG-H540'
 'LG-H542 Build/LRX22G' 'LG-H542 Build/MRA58K' 'LG-H650 Build/LMY47V'
 'LG-H650 Build/MRA58K' 'LG-H700 Build/NRD90U' 'LG-H811 Build/MRA58K'
 'LG-H815 Build/MRA58K' 'LG-H820' 'LG-H830' 'LG-H840 Build/MMB29M'
 'LG-H840 Build/NRD90U' 'LG-H870 Build/NRD90U' 'LG-H872 Build/NRD90U'
 'LG-H910 Build/NRD90M' 'LG-H918 Build/NRD90M' 'LG-H932' 'LG-H933'
 'LG-H990 Build/NRD90M' 'LG-K200 Build/MXB48T' 'LG-K212'
 'LG-K220 Build/MXB48T' 'LG-K240 Build/MXB48T' 'LG-K350' 'LG-K371'
 'LG-K410 Build/LRX22G' 'LG-K428 Build/MMB29M' 'LG-K430 Build/MRA58K'
 'LG-K450 Build/MXB48T' 'LG-K500 Build/MMB29M' 'LG-K530 Build/MMB29M'
 'LG-K530 Build/NRD90U' 'LG-K580 Build/MRA58K' 'LG-LG870'
 'LG-LS777 Build/NRD90U' 'LG-LS993 Build/NRD90U' 'LG-LS997 Build/NRD90M'
 'LG-M150' 'LG-M153 Build/MXB48T' 'LG-M200 Build/NRD90U'
 'LG-M210 Build/NRD90U' 'LG-M250 Build/NRD90U' 'LG-M257'
 'LG-M320 Build/NRD90U' 'LG-M400 Build/NRD90U' 'LG-M700 Build/NMF26X'
 'LG-M710 Build/NRD90U' 'LG-P714' 'LG-SP320' 'LG-TP260 Build/NRD90U'
 'LG-TP450 Build/NRD90U' 'LG-V410/V41020c' 'LG-X165g Build/LRX21M'
 'LG-X180g Build/LMY47I' 'LG-X210 Build/LMY47I' 'LG-X220 Build/LMY47I'
 'LG-X230 Build/MRA58K' 'LG-X240 Build/MRA58K' 'LGL163BL'
 'LGL164VL Build/NRD90U' 'LGL83BL' 'LGLS676 Build/MXB48T' 'LGLS770'
 'LGLS775 Build/NRD90U' 'LGLS990' 'LGLS992' 'LGMP260 Build/NRD90U'
 'LGMP450 Build/NRD90U' 'LGMS210 Build/NRD90U' 'LGMS330 Build/LMY47V'
 'LGMS395' 'LGMS428' 'LGMS550 Build/MXB48T' 'LGMS550 Build/NRD90U'
 'LGMS631' 'LGMS631 Build/MRA58K' 'LIMIT' 'LM-X210(G' 'LS5' 'Lenovo'
 'Lenovo A2016b30 Build/MRA58K' 'Lenovo A6020l37 Build/LMY47V'
 'Lenovo K33b36 Build/MMB29M' 'Lenovo K33b36 Build/NRD90N'
 'Lenovo PB1-750M Build/S100' 'Lenovo PB2-650Y Build/MRA58K'
 'Lenovo PB2-670Y Build/MRA58K' 'Lenovo TB3-710F Build/LRX21M'
 'Lenovo TB3-710I Build/LMY47I' 'Lenovo YT3-850F Build/MMB29M'
 'Lenovo YT3-850M Build/MMB29M' 'Lenovo-A6020l36 Build/LMY47V'
 'LenovoA3300-GV Build/JDQ39' 'Linux i686' 'Linux x86_64' 'M4'
 'M4 SS4451 Build/LMY47D' 'M4 SS4453 Build/MMB29M'
 'M4 SS4456 Build/LMY47V' 'M4 SS4457 Build/MRA58K'
 'M4 SS4457-R Build/NRD90M' 'M4 SS4458 Build/MMB29M' 'MALC'
 'MHA-L09 Build/HUAWEIMHA-L09' 'MHA-L29 Build/HUAWEIMHA-L29'
 'MOT-A6020l37 Build/LMY47V' 'MTT' 'MYA-L03 Build/HUAWEIMYA-L03'
 'MYA-L13 Build/HUAWEIMYA-L13' 'MacOS' 'Mi A1 Build/N2G47H'
 'Mi A1 Build/OPR1.170623.026' 'Microsoft' 'Moto'
 'Moto C Build/NRD90M.054' 'Moto C Build/NRD90M.057'
 'Moto C Build/NRD90M.063' 'Moto C Build/NRD90M.070'
 'Moto C Plus Build/NRD90M.05.034' 'Moto E (4) Build/NDQS26.69-23-2-3'
 'Moto E (4) Build/NDQS26.69-64-2' 'Moto E (4) Build/NMA26.42-19'
 'Moto E (4) Build/NMA26.42-69' 'Moto E (4) Plus Build/NMA26.42-142'
 'Moto E (4) Plus Build/NMA26.42-152' 'Moto E (4) Plus Build/NMA26.42-69'
 'Moto G (4) Build/NPJ25.93-14' 'Moto G (4) Build/NPJ25.93-14.5'
 'Moto G (4) Build/NPJ25.93-14.7' 'Moto G (4) Build/NPJS25.93-14-10'
 'Moto G (4) Build/NPJS25.93-14-13' 'Moto G (4) Build/NPJS25.93-14-15'
 'Moto G (4) Build/NPJS25.93-14-18' 'Moto G (4) Build/NPJS25.93-14-8'
 'Moto G (5) Build/NPP25.137-33' 'Moto G (5) Build/NPP25.137-38'
 'Moto G (5) Build/NPP25.137-72' 'Moto G (5) Build/NPP25.137-82'
 'Moto G (5) Build/NPP25.137-93' 'Moto G (5) Build/NPPS25.137-15-11'
 'Moto G (5) Build/NPPS25.137-93-4' 'Moto G (5) Build/NPPS25.137-93-8'
 'Moto G (5) Plus Build/NPN25.137-72' 'Moto G (5) Plus Build/NPN25.137-82'
 'Moto G (5) Plus Build/NPN25.137-92'
 'Moto G (5) Plus Build/NPNS25.137-15-11'
 'Moto G (5) Plus Build/NPNS25.137-92-10'
 'Moto G (5) Plus Build/NPNS25.137-92-4'
 'Moto G (5) Plus Build/NPNS25.137-92-8'
 'Moto G (5) Plus Build/NPNS25.137-93-8' 'Moto G (5S'
 'Moto G Play Build/MPIS24.241-15.3-26'
 'Moto G Play Build/MPIS24.241-15.3-7' 'Moto G Play Build/NPI26.48-36'
 'Moto G Play Build/NPIS26.48-36-2' 'Moto G Play Build/NPIS26.48-36-5'
 'Moto X Play Build/NPD26.48-24-1' 'Moto Z (2'
 'Moto Z2 Play Build/NPS26.118-19' 'Moto Z2 Play Build/NPSS26.118-19-11'
 'Moto Z2 Play Build/NPSS26.118-19-14'
 'Moto Z2 Play Build/NPSS26.118-19-4' 'MotoE2 Build/LPCS23.13-56-5'
 'MotoE2(4G-LTE' 'MotoG3 Build/MPI24.65-25' 'MotoG3 Build/MPI24.65-33.1-2'
 'MotoG3 Build/MPIS24.107-55-2-17' 'MotoG3 Build/MPIS24.65-33.1-2-16'
 'MotoG3-TE Build/MPD24.65-33' 'MotoG3-TE Build/MPDS24.65-33-1-3'
 'MotoG3-TE Build/MPDS24.65-33-1-30' 'N9560 Build/NMF26F'
 'NEM-L51 Build/HONORNEM-L51' 'Nexus' 'Nexus 5 Build/M4B30Z'
 'Nexus 5X Build/OPR4.170623.006' 'Northwell' 'ONE'
 'ONE A2003 Build/MMB29M' 'ONE TOUCH 4016A Build/JDQ39'
 'ONE TOUCH 4033A Build/JDQ39' 'ONEPLUS A5010 Build/OPM1.171019.011'
 'ORION' 'P008 Build/NRD90M' 'P4526A Build/NRD90M' 'P5006A Build/NRD90M'
 'P5026A' 'P5046A' 'PRA-LX1 Build/HUAWEIPRA-LX1'
 'PRA-LX3 Build/HUAWEIPRA-LX3' 'PSPC550 Build/LMY47D'
 'PULP 4G Build/LMY47V' 'Pixel' 'Pixel 2 XL Build/OPM2.171019.029'
 'Pixel 2 XL Build/OPM2.171019.029.B1' 'Pixel Build/OPM2.171019.029'
 'Pixel Build/OPM4.171019.016.B1' 'Pixel XL Build/OPM4.171019.016.B1'
 'QTAQZ3 Build/LMY47V' 'QTASUN1 Build/NRD90M' 'R2' 'R831L'
 'RCT6203W46 Build/KOT49H' 'RCT6303W87M7 Build/MRA58K'
 'RCT6513W87 Build/MRA58K' 'REVVLPLUS' 'RNE-L03 Build/HUAWEIRNE-L03'
 'RNE-L21 Build/HUAWEIRNE-L21' 'RNE-L22 Build/HUAWEIRNE-L22'
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# Frequency encoding for DeviceInfo
self_encode_False=['DeviceInfo']
train, test = frequency_encoding(train, test, self_encode_False, self_encoding=False)

# Drop the original column
train.drop('DeviceInfo', axis=1, inplace=True)
test.drop('DeviceInfo', axis=1, inplace=True)

gc.collect()

8004

Pickling Final Train and Test

#pickling datasets
#Save 'train' data to a pickle file named 'train_1=3.pkl'
train.to_pickle(r'C:\Fraud_Data\data\train_3.pkl')

#save 'test' data to a pickle file named 'test_3.pkl'
test.to_pickle(r'C:\Fraud_Data\data\test_3.pkl')

import pandas as pd
# Read the 'train_3.pkl' pickle file and load it into the 'train' DataFrame
train = pd.read_pickle('./train_3.pkl')

# Read the 'test_3.pkl' pickle file and load it into the 'test' DataFrame
test = pd.read_pickle('./test_3.pkl')

Removing Date columns

# Date columns are not indicators
train.drop('TransactionDT', axis=1, inplace=True)
train.drop('DT', axis=1, inplace=True)

test.drop('TransactionDT', axis=1, inplace=True)
test.drop('DT', axis=1, inplace=True)

pd.DataFrame(train.columns).to_clipboard()

# We have 128 independent variables 1 dependent variable last.
len(train.columns)

128

info = train.dtypes
info.to_clipboard()

Defining X sets and Y

# Target variable for training set (y_train)
y_train = train['isFraud']

# Independent variables for training set (X_train)
X_train = train.drop(['isFraud'], axis=1)

# Target variable for test set (y_test)
y_test = test['isFraud']

# Independent variables for test set (X_test)
X_test = test.drop(['isFraud'], axis=1)

Default Model

# Get the count of negative and positive examples
count_negative = (y_train == 0).sum()
count_positive = (y_train == 1).sum()

# Calculate the value of scale_pos_weight
scale_pos_weight = math.sqrt(count_negative / count_positive)

#XGBoost with default parameters
xgb = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb.fit(X_train, y_train)

#prediction
y_pred_xgb = xgb.predict(X_test)

#classification report
print(classification_report(y_test, y_pred_xgb))

#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb, normalize='true'))

              precision    recall  f1-score   support

           0       0.98      0.99      0.98    142535
           1       0.50      0.42      0.46      5100

    accuracy                           0.97    147635
   macro avg       0.74      0.70      0.72    147635
weighted avg       0.96      0.97      0.96    147635

[[0.98502824 0.01497176]
 [0.57686275 0.42313725]]

# Probas for train
y_train_xgb_proba = xgb.predict_proba(X_train)[:, 1]  
train_auc = roc_auc_score(y_train, y_train_xgb_proba)
print(f'Train AUC: {train_auc}')

# Probas for test
y_pred_xgb_proba = xgb.predict_proba(X_test)[:, 1]  
test_auc = roc_auc_score(y_test, y_pred_xgb_proba)
print(f'Test AUC: {test_auc}')

Train AUC: 0.9625262580415187
Test AUC: 0.8849532849516837

# Feature Importance
cols = list( X_train.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()

# Plotting feature importance with all variables (127 vars)
plt.figure(figsize=(20, 10))
sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", ascending=False))
plt.title('XGB Most Important Features')
plt.tight_layout()
plt.show()

Second Model with first 50 important features and default parameters

# Select the top 50 important features from X_train
selected_features = feature_imp.head(50)['Feature'].tolist()

# Creating new x dataframes
X_train_2 = X_train[selected_features]
X_test_2 = X_test[selected_features]

# Run the default model with the new feature set
#XGBoost with default parameters
xgb_2 = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb_2.fit(X_train_2, y_train) # fitting with X_train_2

#prediction
y_pred_xgb_2 = xgb_2.predict(X_test_2)

#classification report
print(classification_report(y_test, y_pred_xgb_2))

#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb_2, normalize='true'))

              precision    recall  f1-score   support

           0       0.98      0.98      0.98    142535
           1       0.47      0.41      0.44      5100

    accuracy                           0.96    147635
   macro avg       0.73      0.70      0.71    147635
weighted avg       0.96      0.96      0.96    147635

[[0.98364612 0.01635388]
 [0.58705882 0.41294118]]

# Probas for train
y_train_xgb_proba_2 = xgb_2.predict_proba(X_train_2)[:, 1]  
train_auc = roc_auc_score(y_train, y_train_xgb_proba_2)
print(f'Train AUC: {train_auc}')

# Probas for test
y_pred_xgb_proba_2 = xgb_2.predict_proba(X_test_2)[:, 1]  
test_auc = roc_auc_score(y_test, y_pred_xgb_proba_2)
print(f'Test AUC: {test_auc}')

Train AUC: 0.9514796162751223
Test AUC: 0.8728808582962423

# Feature Importance
cols = list( X_train_2.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb_2.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()

Third Model with first 40 important features and default parameters

# Select the top 30 important features from X_train
selected_features = feature_imp.head(40)['Feature'].tolist()

# Creating new x dataframes
X_train_3 = X_train[selected_features]
X_test_3 = X_test[selected_features]

# Run the default model with the new feature set
#XGBoost with default parameters
xgb_3 = XGBClassifier(objective='binary:logistic', random_state=1003, eval_metric='auc', scale_pos_weight=scale_pos_weight)
xgb_3.fit(X_train_3, y_train) # fitting with X_train_2

#prediction
y_pred_xgb_3 = xgb_3.predict(X_test_3)

#classification report
print(classification_report(y_test, y_pred_xgb_3))

#confusion matrix
print(confusion_matrix(y_test, y_pred_xgb_3, normalize='true'))

              precision    recall  f1-score   support

           0       0.98      0.98      0.98    142535
           1       0.45      0.42      0.44      5100

    accuracy                           0.96    147635
   macro avg       0.72      0.70      0.71    147635
weighted avg       0.96      0.96      0.96    147635

[[0.98182201 0.01817799]
 [0.58019608 0.41980392]]

# Probas for train
y_train_xgb_proba_3 = xgb_3.predict_proba(X_train_3)[:, 1]  
train_auc = roc_auc_score(y_train, y_train_xgb_proba_3)
print(f'Train AUC: {train_auc}')

# Probas for test
y_pred_xgb_proba_3 = xgb_3.predict_proba(X_test_3)[:, 1]  
test_auc = roc_auc_score(y_test, y_pred_xgb_proba_3)
print(f'Test AUC: {test_auc}')

Train AUC: 0.9501872964334457
Test AUC: 0.8720227291955123

# Feature Importance
cols = list( X_train_3.columns)
feature_imp = pd.DataFrame(sorted(zip(xgb_3.feature_importances_, cols), key=lambda x: x[0], reverse=True), columns=['Value', 'Feature'])
feature_imp.to_clipboard()

# Plotting feature importance with selected 40 params
plt.figure(figsize=(20, 10))
sns.barplot(x="Value", y="Feature", data=feature_imp.sort_values(by="Value", ascending=False))
plt.title('XGB Most Important Features')
plt.tight_layout()
plt.show()